ΠΕΡΙΒΑΛΛΟΝΤΙΚΗ ΠΡΟΒΛΕΨΗ ΜΕ ΜΕΘΟΔΟΥΣ ΜΗΧΑΝΙΚΗΣ ΜΑΘΗΣΗΣ

ΠΕΡΙΒΑΛΛΟΝΤΙΚΗ ΠΡΟΒΛΕΨΗ ΜΕ ΜΕΘΟΔΟΥΣ ΜΗΧΑΝΙΚΗΣ

ΜΑΘΗΣΗΣ

ΗΛΙΑΣ Θ ΚΑΛΑΠΑΝΙΔΑΣ

ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ2003

Η διάρθρωση της παρουσίασης

1 Μηχανική Μάθηση2 Περιβαλλοντική Πληροφορική3 Προ-επεξεργασία4 Μείωση των διαστάσεων5 Εφαρμογή των αλγορίθμων6 Κατανεμημένη υλοποίηση7 Προς ένα ολοκληρωμένο σύστημα8 Συμπεράσματα

1 ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ

bull Ορισμός

bull Μορφοποίηση συνόλου δεδομένων

bull Εφαρμογές

bull Αλγόριθμοι

1Μηχανική Μάθηση1Μηχανική Μάθηση

ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ

Ορισμός

laquoΗ αναζήτηση σε ένα χώρο πιθανών υποθέσεων εκείνης της υπόθεσης που ταιριάζει καλύτερα στα υπό εξέταση δεδομένα και στην πιθανώς προϋπάρχουσα γνώσηraquo


Ένα παράδειγμα

bull Αναγνώριση του λουλουδιού της ίριδαςbull Πιθανές κλάσεις που αυτό ανήκει

ndash Iris Setosandash Iris Virginicandash Iris Versicolour

bull 4 ιδιότητεςndash Μήκος πετάλουndash Πλάτος πετάλουndash Μήκος σεπάλουndash Πλάτος σεπάλου


Μορφοποιημένο σύνολο δεδομένων

57 44 15 04 Iris-setosa





hellip hellip hellip hellip hellip

7 32 47 14 Iris-versicolor



63 33 6 25 Iris-virginica



Ιδιότητες Μεταβλητή κλάσης

Παράδειγμα

Μήκος σεπάλου

Πλάτος σεπάλου

Πλάτος πετάλου

Μήκος πετάλου

Εκπαίδευση του αλγόριθμου






Σύνολο εκπαίδευσης

Αλγόριθμος

Εκπαιδευμένο μοντέλο

αλγορίθμου

Υrsquo = f(X) + ε

TSE
Η μανιβέλα είναι δυνατόν να γυρίζει γύρω-γύρω με animation

Αξιολόγηση του αλγόριθμου





Σύνολο αξιολόγησης

Υrsquo = f(X) + ε


Μέτρα Απόδοσης Εκπαιδευμένου Μοντέλου


Νέο παράδειγμα

Εφαρμογή του αλγόριθμου57 44 15 04

Iris-setosa

Υrsquo = f(X) + ε


Πρόβλεψη


Εφαρμογές

bull Χρηματιστήριο

bull Βιομηχανία

bull Περιβάλλον

bull Βιοτεχνολογία Γενετική

bull Τραπεζικός Ασφαλιστικός τομέας


Αλγόριθμοι

bull Είδη ΜηχΜάθησηςndash Υπό επίβλεψηndash Χωρίς επίβλεψη

bull Τύποι προβλημάτων υπό επίβλεψηndash Παλινδρόμησηςndash Ταξινόμησης



bull K-κοντινότεροι γείτονεςbull Παλινδρόμησηbull Τεχνητά Νευρωνικά Δίκτυαbull Περιπτωσιακός Λογισμόςbull Δέντρα Απόφασηςbull Δίκτυα Bayesbull Γενετικοί Αλγόριθμοι


Κ-κοντινότεροι γείτονες


R


bull Πλεονεκτήματαndash Απλή υλοποίησηndash Αποδεκτά αποτελέσματα σε μεγάλο πεδίο

εφαρμογώνndash Πολύχρονη εμπειρία χρήσης

bull Μειονεκτήματαndash Χρονοβόρος σε μεγάλα σύνολα δεδομένων


Παλινδρόμηση


Τεχνητά Νευρωνικά Δίκτυα


Πρώτη κρυμμένη

ζώνη

Δεύτερη κρυμμένη

ζώνη

Είσοδος 3Είσοδος 1Είσοδος 2 Είσοδος 4

Είσοδος 5Είσοδος 6

Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος


bull Πλεονεκτήματαndash Ανοχή στον θόρυβοndash Ικανότητα γενίκευσης

bull Μειονεκτήματαndash Δυσνόητο μοντέλο (μαύρο κουτί)ndash Δυσανάλογος χρόνος εκπαίδευσης


Περιπτωσιακός Λογισμός


Νέα περίπτωση

Πρόβλημα

Παλαιά περίπτωσηΝέα

περίπτωση

Λυμένη περίπτωση

Δοκιμασμένη διορθωμένη

λύση

Διδαχθείσα περίπτωση

Ανάκληση περιπτώσεων

Επαναχρησιμοποίηση παλιών λύσεων

Προσαρμογή λύσης

Αιτιολόγηση λύσης

Γενική Γνώση

Παλαιά περίπτωσηΠαλαιά περίπτωσηΠαλαιά

περίπτωση

Παλαιές περιπτώσεις

Προτεινόμενη λύσηΕγκεκριμένη λύση

Δέντρα Απόφασης


Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ

P(X1 X2 X3 X4 X5 Y) = P(X1)P(X2)P(X3|X1 X2)P(Y|X1 X2 X3)P(X4|Y) P(X5|X4 Y)

Γενετικοί Αλγόριθμοι


Αρχικός πληθυσμός

Αξιολόγηση συνάρτησης

προσαρμογής

Επιλογή

Διασταύρωση

Μετάλλαξη

Γέννηση νέου πληθυσμού

Επιτεύχθηκαν τα κριτήρια

βελτιστοποίησης

Εξαγωγή λύσης

Ναι

Όχι

Περιβαλλοντική Πληροφορική

bull Θέματαndash Ολοκληρωμένες πλατφόρμες αξιολόγησης και μελέτες

περιπτώσεωνndash Ανάλυση περιβαλλοντικών συστημάτωνndash Πρόβλεψη περιβαλλοντικών αλλαγώνndash Παρακολούθηση του περιβάλλοντος σε πραγματικό χρόνοndash Πιστοποίηση ποιότητας περιβαλλοντικών μοντέλωνndash Εφαρμογές μοντελοποίησης με agents και εξομοίωσης σε

περιβαλλοντικά συστήματα

2Περιβαλλοντική Πληροφορική2Περιβαλλοντική Πληροφορική


bull Πεδία Εφαρμογήςndash Ποιότητα Αέραndash Ποιότητα Εδάφουςndash Ποιότητα Υδάτωνndash Διαχείριση Αποβλήτων


Αέρια Ρύπανση

bull Παρακολούθηση και πρόβλεψη των επιβλαβών ρύπωνndash Ο3

ndash ΝΟx

ndash PM10 Σωματίδιαndash Καπνός


Τα προβλήματα του ΠΕΡΠΑ

bull Πρόβλεψη της μέγιστης συγκέντρωσης για τους ρύπουςndash ΝΟ2

ndash Ο3

bull Για τις χρονικές περιόδουςndash Ίδια ημέρα μετά τις 10 πμndash Επόμενη ημέρα


Π1Π2 Πρόβλεψη την ίδια ημέρα

0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3

ΠρόβλεψηΣυλλογή δεδομένων

O3 m

ax

NO

2 max


Π3Π4 Πρόβλεψη για την επόμενη ημέρα

0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max


Προ-επεξεργασία των δεδομένων

bull Αντιμετώπιση θορύβου

bull Αντικατάσταση ελλιπών τιμών

3Προ-επεξεργασία3Προ-επεξεργασία

Μελέτη θορύβου

bull 2 μοντέλα θορύβουndash Ύπαρξη θορύβου μόνο στις ιδιότητεςndash Ύπαρξη θορύβου και στις ιδιότητες και

στην εξαρτημένη μεταβλητή

bull Εκτίμηση απόκλισης τιμής ιδιότητας λόγω θορύβου


piii zxx

Αποτελέσματα μελέτης θορύβου- 1ο μοντέλο

-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05

IB1 - Attribute IBk -K 9 - Attribute LinearRegression - Attribute M5Prime - Attribute NeuralNetwork - Attribute ZeroR - Attribute

Drop Page Fields Here

Average of Root mean squared error (diff)

Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05

IB1 - AttributeClassVar IBk -K 9 - AttributeClassVar LinearRegression - AttributeClassVar

M5Prime - AttributeClassVar NeuralNetwork - AttributeClassVar ZeroR - AttributeClassVar



Noise Level

Scheme

Noise Model


Μελέτη μεθόδων αντικατάστασης ελλιπών τιμών

bull Ελλιπής τιμή στο παράδειγμα p στην ιδιότητα Χi που ανήκει στην κλάση Υj

1 Αντικατάσταση με τη μέση τιμή της ιδιότητας2 Αντικατάσταση με τη μέση τιμή της ιδιότητας ανάμεσα στα

παραδείγματα που ανήκουν στην κλάση Υj

3 Δημιουργία k συνόλων δεδομένων από το αρχικό σύνολο όπου οι ελλιπείς τιμές έχουν αντικατασταθεί από κατάλληλα υπολογισμένες τιμές με τη μέθοδο της μεγιστοποίησης της προσδοκίας (Expecation Maximization ndashEM)

4 Εφαρμογή επίμονου προβλέπτη δηλαδή αντικατάσταση με την τιμή Xi|p-1


Αποτελέσματα ελλιπών τιμών-RMSE

-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05

1avg 3avg amelia mean meanclass


MV Level

Replacement Method


Αποτελέσματα ελλιπών τιμώνndashΧρόνος ανάπτυξης μοντέλου

-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05


Average of Time taken to build model (diff)

MV Level

Replacement Method


Μείωση των διαστάσεων

bull Ορισμός γιατί και πωςbull Μέθοδοι επιλογής ιδιοτήτωνbull Περιτύλιξη με γενετικό αλγόριθμοbull Λεπτομέρειες της υλοποίησηςbull Ρύθμιση των παραμέτρωνbull Αποτελέσματαbull Σύγκριση με άλλη μέθοδο επιλογής ιδιοτήτων

4Μείωση των Διαστάσεων4Μείωση των Διαστάσεων

Επιλογή των ιδιοτήτων57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51

bull 2ν πιθανοί συνδυασμοί

bull νμ(ν-μ) πιθανοί συνδυασμοί από μ ιδιότητες


Πως ορίζεται η ιδιότητα

bull Ορισμός ιδιότηταςndash Η συγκέντρωση του ρύπου P στο σταθμό

μέτρησης S την ώρα H Ο ημέρες πριν την ημέρα της πρόβλεψης D (D-O)

bull Ήndash Η συγκεντρωτική συνάρτηση F της συγκέντρωσης

του ρύπου P στο σταθμό μέτρησης S από την ώρα H1 έως H2 Ο ημέρες πριν την ημέρα της πρόβλεψης D (D-O)


Λόγοι επιλογής ιδιοτήτων

bull Πάρα πολλές ιδιότητες από τις οποίες μπορεί να κατασκευαστεί ένα σύνολο δεδομένων

bull Μερικές ιδιότητες είναι περιττές ή μη σχετιζόμενες με την εξαρτημένη μεταβλητή

bull Απλοποίηση του μοντέλου πολλές φορές βελτιώνει την ικανότητα γενίκευσης (κανόνας του Occam)

bull Η ανεύρεση των σημαντικών ιδιοτήτων συμβάλει στην κατανόηση του προβλήματος


Μέθοδοι Αντιμετώπισης

bull Μέθοδοιndash Φιλτραρίσματοςndash Περιτύλιξης


Μέθοδος φιλτραρίσματος


Αποθήκηιδιοτήτων

Υποσύνολοιδιοτήτων

υπό αξιολόγηση

Επιλογή υποσυνόλουιδιοτήτων

Υπολογισμός μέτρωνποιότητας των ιδιοτήτων

Επαύξηση ή μείωση των ιδιοτήτων

Είναι αποδεκτό τοαξιολογημένουποσύνολο

ΌχιΝαιΕπιλεγμένουποσύνολοιδιοτήτων

Επιλογή νέου υποσυνόλουιδιοτήτων

Μέθοδοςφιλτραρίσματος

Γενετικός Αλγόριθμος

Μέθοδος περιτύλιξης






Εκπαιδευμένο Μοντέλο

Σύνολοδεδομένων

Κατασκευή συνόλου δεδομένων

Αξιολογημένο Μοντέλο

Εκπαίδευση Αλγορίθμου

Αξιολόγηση Αλγορίθμου

Είναι αποδεκτό τοαξιολογημένο

μοντέλο




Περιτύλιξη με Γενετικό Αλγόριθμο

bull Δημιουργία πληθυσμού από υποσύνολα ιδιοτήτων

bull Συνάρτηση προσαρμογής δύο κριτηρίωνndash Απόδοση συνόλου δεδομένων του υποσυνόλου

ιδιοτήτων στον αλγόριθμο κ-κοντινότερων γειτόνων

ndash Μέγεθος υποσυνόλου ιδιοτήτωνbull Εξέλιξη πληθυσμού ανά γενιά εφαρμόζοντας

γενετικούς τελεστές



11 22 33 4400

B1B1 B2B2 B3B3 B4B4 B5B5

Δυναμικός πίνακας των συμβολοσειρών των ιδιοτήτων

Επέλεξε τη συγκέντρωση του ΝΟ2 στο σταθμό μέτρησης με

κωδικό 104 (Γεωπονική) στις 5 μμ

000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104

F = αδιάφορο

H1 = 1700 O = 0

H2 = αδιάφορο

ΣημαίαΣυγκεντρωτικής ιδιότητας = 0

Επέλεξε τη μέση συγκέντρωση του

SΟ2 στο σταθμό μέτρησης με κωδικό 108 (Περιστέρι) από τις 4 πμ μέχρι τις 9 μμ

100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108

F = Μέση τιμή-Avg

H1 = 400 O = -2

H2 = 2100


Η Κωδικοποίηση του γενότυπου

Η συνάρτηση προσαρμογής

bull Πολυαντικειμενική (δύο στόχοι)bull Ελαχιστοποίηση σφάλματος πρόβλεψης του

αλγόριθμου κ-κοντινότερων γειτόνωνbull Ελαχιστοποίηση αριθμού ιδιοτήτων στο

υποσύνολο ιδιοτήτων της λύσης

nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1


Οι παράμετροι της συνάρτησης προσαρμογής a b


Το τροποποιημένο μέτρο αξιολόγησης μοντέλων WSE

bull et = Xt ndash Ft

bull Xt είναι η πραγματική τιμή της εξαρτημένης μεταβλητής

bull Ft είναι η εκτίμηση της Xt από τον αλγόριθμο


n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2

Ρύθμιση του μεγέθους πληθυσμού


Best Solution for different Population Sizes

0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90

MaxFitness AvgFitness MinFitness BestWSE BestFeature

Ρύθμιση συντελεστών a b


Best Solution for different Coefficient pairs

0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80


Ρύθμιση του ρυθμού μετάλλαξηςBest Solution for different Mutation percentage

0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80



Ρύθμιση αριθμού ελίτ μελώνBest Solution for different Elite numbers

0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90

MaxFitness AvgFitness MinFitness BestWSE BestFeatures


Η μέθοδος CFS

bull Προσθαφαίρεση ιδιοτήτων στο επιλεγμένο υποσύνολο ιδιοτήτων με κριτήριαndash Υψηλή συσχέτιση των ιδιοτήτων με την

εξαρτημένη μεταβλητή κλάσηςndash Χαμηλή ενδοσυσχέτιση ανάμεσα στις

ιδιότητες του υποσυνόλου


)1( ii

cis

rkkk

rkG

Σύγκριση των δύο μεθόδων


000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable

IB1 IB3 IB9 LinearRegression

M5Prime

GA Mean absolute error GA Root mean squared error

CFS Mean absolute error CFS Root mean squared error

Η μηχανική μάθηση στην πρόβλεψη της αέριας ρύπανσης

bull 4 επίπεδα της εξαρτημένης μεταβλητήςbull 3 αλγόριθμοι επίλυσης

ndash σύστημα lsquoΝΕΜΟrsquo Περιπτωσιακού Λογισμούndash Νευρωνικό Δίκτυο MLPndash Δέντρο απόφασης CART

bull Συνδυασμένος ταξινομητής

5Λύνοντας το πρόβλημα Π15Λύνοντας το πρόβλημα Π1

Το σύστημα ΠΛ ΝΕΜΟ (1)

Όμοιες περιπτώσεις

Επιλογέας κοντινών περιπτώσεων

Φίλτρο κοντινών περιπτώσεων

Προσαρμογέας κοντινών περιπτώσεων

Προβλέπτης λύσης

Προτεινόμενη λύση

ΝΕΜΟ

Βάση περιπτώσεων

Κοντινέςπεριπτώσεις

Βάση Γνώσης



Το Τεχνητό Νευρωνικό Δίκτυοηλ ακτινοβολία

αναστροφή

βροχόπτωση

άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ

NO2 στις 10 πμ

NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος

Το δέντρο απόφασης (1)NOat10

NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036


Το δέντρο απόφασης (2)

0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

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Ο συνδυασμένος ταξινομητής

bull Πλειοψηφικός Συνδυαστής των τριών ταξινομητών ΝΕΜΟ ΔΑ ΤΝΔ


Αποτελέσματα Πίνακες Σύγχυσης (1)


Επίπεδο 1Επίπεδο 2


Εκτιμήθηκε ως επίπεδο 1




134

38

522

122

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8

4

0

20

40

60

80

100

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140

Πίνακας Σύγχυσης ΝΕΜΟ

Εκτιμήθηκε ως επίπεδο 1 Εκτιμήθηκε ως επίπεδο 2









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Πίνακας Σύγχυσης ΔΑ CART











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138

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120

140

Πίνακας Σύγχυσης Συνδυασμένος Ταξινομητής




Στατιστικά για τους 4 αλγόριθμους

0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

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ode

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CART NeuralNet NEMO ML Ensemble


Σύγκριση με συστήματα πρόβλεψης του εξωτερικού


0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

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recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F

London 1997 Sydney ML Ensemble

Κατανεμημένη υλοποίηση Το σύστημα D-NEMO

6Το6Το κατανεμημένο σύστημα κατανεμημένο σύστημα DNEMODNEMO

bull Απαιτήσεις

bull Αρχιτεκτονική

bull Η πλατφόρμα Lalo

bull Η γλώσσα KQML

bull Συμπεριφορά των agents

bull Υποκύκλοι λειτουργίας

Απαιτήσεις Περιπτώσεις χρήσηςDNEMO

Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source


Αρχιτεκτονική


aStation StationAgent

dataFeeder dataAgentaModel ModelAgent

Timer TimeWatcher

48

bid

_off

er()

Controller ControllerAgent

11 maintenance_time()41 prediction_time()

42 prediction_time()12 maintenance_time()

43 return_bid()13 evaluate_models()

44 return_bid()

15 evaluate_model()

49 store_bid_offer()

410 store_winner_offer()

16 return_efficiency()22 make_trainset()45 make_testset()17 efficiency_ok()

18 efficiency_failure()23 trainset_ok()

24 trainset_failure()46 testset_ok()

Ο Lalo Agent


Άλλοι agents

Περιβάλλον του agent

Επίπεδο Βάσεων Κανόνων

Κανόνες Εκτέλεσης Κανόνες Επικοινωνίας Γενικοί Κανόνες

Επίπεδο Νοητικής Κατάστασης

Ικανότητες Πεποιθήσεις

Αποφάσεις Υποχρεώσεις

Ικανότητες συναναστροφών

Επίπεδο Συμπεριφοράς

Αποστολή μηνυμάτων Επεξεργασία μηνυμάτων

Εκτέλεση ενέργειας Χρονοπαρακολούθηση

Επικοινωνιακό Επίπεδο

Διευθύνσεις συναναστροφών

Ενταμιευτής εισόδου

Δρομολογητής εισόδου

Δρομολογητής εξόδου

Η γλώσσα KQML (1)

bull Τυποποιημένα μηνύματα ndashεκτελεστικάbull Παράμετροι εκτελεστικών

ndash senderndash receiverndash reply-withndash in-reply-tondash contentndash languagendash ontology



bull Σημαντικά εκτελεστικά που υποστηρίζει το Lalondash AchieveUnachievendash Ask-onendash Ask-ifndash Denyndash RegisterUnregisterndash Replyndash TellUntellndash Transport-address


Υποκύκλος πρωινής συντήρησης

every Model ModelAgentTimer TimeWatcher aController ControllerAgent every Station StationAgent dataFeeder dataAgent

11 maintenance_time()


13 evaluate_models()

15 evaluate_model()

[model trained] 16 return_efficiency()

17 efficiency_ok()

18 efficiency_failure() OR

19 efficiency()

14 update_tables()

[model NOT trained] 22 make_trainset()

23 trainset_ok()

24 trainset_failure() OR morning_update()

morning_update()


Υποκύκλος απόφασηςaModel ModelAgent dataFeeder dataAgent

[needs retraining] 21 train_model()

22 make_trainset()

24 trainset_failure()

23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()


Υποκύκλος πρόβλεψηςTimer TimeWatcher aController ControllerAgent every Station StationAgent every Model ModelAgent dataFeeder dataAgent

41 prediction_time()


43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()

47 testset_failure()

OR

48 bid_offer()



wait 10 seconds

Testset Requery Procedurefollows

gather all predictionoffers from every

correspodent modelagent

query_testset()

run_model()

store_offer()

store_final()

issue_prediction()


Αποτελέσματα


Προς ένα ολοκληρωμένο επιχειρησιακό κέντρο πρόγνωσης

της αέριας ρύπανσης

Απαιτήσεις Περιπτώσεις χρήσης

77Προς ένα ολοκληρωμένο σύστημαΠρος ένα ολοκληρωμένο σύστημα

Air QualityManagement

Operational Centre

Gather Data fromData Sources

EMY

PERPA sensors

National Observatory

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

Air PollutionEpisodes Predictio

n

Control Strategyof Prevention

Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public

Predictive Algorithms Expert

Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo

Supervise and Control Operational Centre

Σταθερότητα Βιωσιμότητα (1)


Timer UIAgent aController ControllerAgent everyStation StationAgent everyModel ModelAgent

activity_query() activity_query() activity_query()

[agent is active] status_response()





Watcher UIAgent nonUIAgent MachineAgent

activity_query()

[no response from nonUIAgent] activity_query()

x seconds delaywaiting for response

activity_query()

MachineAgent andnonUIAgent locatedin the same machine

if MachineAgent responds toactivity_query then nonUIAgentprobably has crashed or is busy

else the delay is dueto the network channel or

the machine is unavailable

[MachineAgent responded] revive_agent()

nonUIAgentcreate_agent()

otherMachineAgent

[MachineAgent did not respond] revive_agent()


status_response()

status_response()

Machine A Machine B

Ολοκληρώνοντας

bull Μελέτη του κύκλου εφαρμογής της Μηχανικής Μάθησης σε ένα περιβαλλοντικό πρόβλημα

bull Ανάπτυξη συστήματος ΠΛ ΝΕΜΟbull Ανάπτυξη ειδικού σκοπού ΓΑ για επιλογή

ιδιοτήτωνbull Σχεδίαση και υλοποίηση συστήματος

πολλαπλών agents για την ολοκλήρωση και αυτοματοποίηση της πρόβλεψης των αέριων ρύπων σε καθημερινή βάση

88ΣυμπεράσματαΣυμπεράσματα

Μελλοντικές Επεκτάσεις

bull Ολοκλήρωση της εφαρμογής των αλγόριθμων σε όλα τα προβλήματα Π1-Π4

bull Υλοποίηση υποσυστήματος πρόβλεψης και ενοποίηση σε επιχειρησιακό κέντρο παρακολούθησης και πρόβλεψης αέριας ρύπανσης

bull Επέκταση της μεθοδολογίας σε άλλα περιβαλλοντικά προβλήματα


Η διάρθρωση της παρουσίασης

1 Μηχανική Μάθηση2 Περιβαλλοντική Πληροφορική3 Προ-επεξεργασία4 Μείωση των διαστάσεων5 Εφαρμογή των αλγορίθμων6 Κατανεμημένη υλοποίηση7 Προς ένα ολοκληρωμένο σύστημα8 Συμπεράσματα


bull Ορισμός






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Ναι

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ndash ΝΟx





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0

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100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

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0

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40

60

80

100

120

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NO2 NO SO2 CO O3


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400

500

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700

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000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

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lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

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lt=036 gt036



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Accur

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0001000

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No

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ode

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F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













bull Ορισμός






Ορισμός





































Υrsquo = f(X) + ε

TSE







Υrsquo = f(X) + ε






Iris-setosa

Υrsquo = f(X) + ε


Πρόβλεψη


Εφαρμογές
















R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

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pp

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0

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10

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20

25

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35

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NO2 NO SO2 CO O3


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piii zxx


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Noise Level

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0

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10

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Noise Level

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Noise Model










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54

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11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







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)1( ii

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000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

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ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













Ορισμός





































Υrsquo = f(X) + ε

TSE







Υrsquo = f(X) + ε






Iris-setosa

Υrsquo = f(X) + ε


Πρόβλεψη


Εφαρμογές
















R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B














































Υrsquo = f(X) + ε

TSE







Υrsquo = f(X) + ε






Iris-setosa

Υrsquo = f(X) + ε


Πρόβλεψη


Εφαρμογές
















R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B









































Υrsquo = f(X) + ε

TSE







Υrsquo = f(X) + ε






Iris-setosa

Υrsquo = f(X) + ε


Πρόβλεψη


Εφαρμογές
















R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B






















Υrsquo = f(X) + ε

TSE







Υrsquo = f(X) + ε






Iris-setosa

Υrsquo = f(X) + ε


Πρόβλεψη


Εφαρμογές
















R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B


















Υrsquo = f(X) + ε






Iris-setosa

Υrsquo = f(X) + ε


Πρόβλεψη


Εφαρμογές
















R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B














Iris-setosa

Υrsquo = f(X) + ε


Πρόβλεψη


Εφαρμογές
















R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B












Εφαρμογές
















R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B





















R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B

















R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B














R











ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B






















ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B

















ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B















ζώνη


ζώνη



Είσοδος 7

Είσοδος 8

Είσοδος 9

Είσοδος 10

Έξοδος








Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B



















Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B















Πρόβλημα


περίπτωση



λύση








περίπτωση





Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B














Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B












Δίκτυα Bayes


Χ1 Χ2

Χ3

Χ4

Χ5

Υ







Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B

















Επιλογή


Μετάλλαξη





Ναι

Όχι











ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B






















ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B

















ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B














ndash ΝΟx





ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B














ndash Ο3




0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

20

40

60

80

100

120

140

pp

b

NO2 NO SO2 CO O3


O3 m

ax

NO

2 max



0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

pp

b

0

5

10

15

20

25

30

35

40

pp

b

NO2 NO SO2 CO O3


O3 m

axNO

2 max











piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B





















piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B

















piii zxx


-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













-2

-1

0

1

2

3

4

5

6

7

8

0 005 01 02 03 04 05




Noise Level

Scheme

Noise Model



0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













0

5

10

15

20

25

30

0 005 01 02 03 04 05





Noise Level

Scheme

Noise Model










-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B




















-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













-012

-01

-008

-006

-004

-002

0

002

004

0 005 01 02 03 05



MV Level

Replacement Method



-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













-25

-2

-15

-1

-05

0

05

1

0 005 01 02 03 05



MV Level

Replacement Method






54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B
















54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

57

54

51

51

49

7

64

63

58

44

39

35

35

3

32

32

33

27

15

13

14

14

14

47

45

6

51

04

04

03

02

02

14

15

25

19

15

13

14

14

14

47

45

6

51













































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B





















































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B















































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B









































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B






































μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B


























μοντέλο












11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B




















11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













11 22 33 4400





000000 00110011 000000 1000010000 0001000010 0000000000 00

P = ΝΟ2 S = 104


H1 = 1700 O = 0





100100 01110111 000000 0001100011 1010010100 0001000010 11

P = ΝΟ2 S = 108


H1 = 400 O = -2

H2 = 2100







nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B
















nA

iAb

nE

iEa

iAiEF

max

)(

max

)(1

))()((

a lt= 1 b lt= 1 a + b = 1









n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B



















n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B

















n

e

RMSE

n

tt

1

2

n

tt

n

ttt

X

Xe

WSE

1

2

1

2




0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B















0

05

1

15

2

25

3

35

4

5 10 20 30 40 50 100

Population Size

Fit

nes

s

0

10

20

30

40

50

60

70

80

90





0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B















0

5

10

15

20

25

30

35

01 09 03 07 05 05 07 03 08 02 09 01 095 005

ab pairs

Fit

nes

s

0

10

20

30

40

50

60

70

80



0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













0

05

1

15

2

25

3

35

10 30 40 50 60 70 80 90

Mutation rate ()

Fit

nes

s

0

10

20

30

40

50

60

70

80




0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













0

05

1

15

2

25

3

35

4

1 2 3 4 5 8 10 15

Elite members

Fit

nes

s

0

10

20

30

40

50

60

70

80

90








)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B

















)1( ii

cis

rkkk

rkG



000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B














000

1000

2000

3000

4000

5000

6000

7000

8000

9000

ZeroR DecisionStump

DecisionTable


M5Prime















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B
























ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B



















ΝΕΜΟ







αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













αναστροφή


άνεμος

NO στις 10 πμ

NO στις 9 πμ

NO στις 8 πμ


NO2 στις 9 πμ

NO2 στις 8 πμ



ζώνη


ζώνη

Έξοδος


NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













NOat10

NOat10

NOat10

Wind

NOat9

Rain

Inversion

NOat9

NO2at8 Wind

Radiation

1(26518)

1(235)

2(4412)

1(83) 2(104) 1(100)

2(101) 1(101)

2(10035)

3(5313)

4(80)

4(124)3(91)

lt=027 gt027

lt=021

gt067

gt021

lt=067

lt=04 gt04

lt=057 gt057

lt=066 gt066

lt=047 gt047

lt=021 gt021

=0 =1

lt=021 gt021 lt=08 gt08

lt=036 gt036



0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













0

021

025

04 057

105 075

027

12

1 2 3

4

34

066

0

1

02

04

06

08

ΝΟ στις10πμ

Θερ

μο

κρα

σια

κή α

νασ

τρο

φή













134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B























134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B




















134

38

522

122

93

8

4

0

20

40

60

80

100

120

140











137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B



















137

40

4221

8 317

8

0

20

40

60

80

100

120

140












137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B



















137

42

8216

8 69

4 4

4

0

20

40

60

80

100

120

140












138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B



















138

38

4124

122

13

2

6

0

20

40

60

80

100

120

140






0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F





0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B














0001000

2000300040005000

600070008000

900010000

Accur

acy

ClassE

rror

No

Episod

e P

recis

ion

No

Episod

e R

ecall

No

Episod

e F

Epis

ode

Pre

cision

Epis

ode

Rec

all

Epis

ode

F











Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B





















Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B













Model Expert

Get New Data

Maintain Models

Predict for Day-0

Predict for Day+1

Data Source






Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B
















Timer TimeWatcher

48

bid

_off

er()





44 return_bid()

15 evaluate_model()






Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B












Ο Lalo Agent


Άλλοι agents




























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B
























15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B




















15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B

















15 evaluate_model()


17 efficiency_ok()


19 efficiency()

14 update_tables()


23 trainset_ok()


morning_update()




22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B














22 make_trainset()


23 trainset_ok()OR



wait 10 seconds

Timer TimeWatcher

query_trainset()

reform_models()





43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B















43 return_bid()

44 return_bid()

45 make_testset()

46 testset_ok()


OR

48 bid_offer()



wait 10 seconds




query_testset()

run_model()

store_offer()

store_final()

issue_prediction()









Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B



















Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B

















Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B















Operational Centre


EMY

PERPA sensors


laquoextendsraquo

laquoextendsraquo

laquoextendsraquo


n


Meters

laquousesraquo

Clear and StoreData

laquousesraquo

Air Quality Expert

Public


Data Source

laquoextendsraquo

laquoextendsraquo

laquoextendsraquo

User

Other Sources

laquoextendsraquo












activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B






















activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B















activity_query()



activity_query()







otherMachineAgent



status_response()

status_response()

Machine A Machine B




























Documents

ΠΕΡΙΒΑΛΛΟΝΤΙΚΗ ΠΡΟΒΛΕΨΗ ΜΕ ΜΕΘΟΔΟΥΣ ΜΗΧΑΝΙΚΗΣ ΜΑΘΗΣΗΣ