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D6.4 S1.4
Validation activities for Scenario 2 –
case Ferrara
Module 1:Analysis of historical series of consumption and weather data
1.1.a - Daily profiles
Study performed on two buildings, both served by district heating:
• Scuole Poledrelli (see DailyProfiles_Poledrelli.pptx)
• Museo di Storia Naturale (see DailyProfiles_MuseoStoriaNaturale.pptx)
Four quantities plotted:
• measured consumption (red line)
• measured external temperature (blue line)
• required periods of comfort (unshaded surfaces)
• deduced heating system turn on time
1.1.b - Daily profiles
Scuole Poledrelli:
• Heating system usually off in the weekend
• Heating turn on is anticipated on Mondays and Tuesdays
Museo di Storia Naturale
• Usually on all days
• very regular turn on/of interval
General remarks
• Temperature profile sometimes is unrealistic or incomplete
• As expected consumption trends are inversely proportional to external temperature, with a delay due to thermal inertia.
• Scuole Poledrelli have a higher consumption, but a correct comparison should be done after normalization with heated surface.
-4
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Scuole Poledrelli Museo di Storia Naturale
Martedì 13/01
Mercoledì 14/01
1.2 - Seasonal profiles
Scuole Poledrelli:
• weekly pattern is clearly visible, with the consumption peaks on Mondays and Tuesdays
• this kind of plot triggers the question, is turning turn off the heating system during weekends more efficient than just living it on?
• the question can be evaluated by measuring and comparing the surface of the "Monday peaks" with that of the "weekend valleys"
• comparison of consumption and temperature curves show
• an inverse proportion on the long term trends
• the possible effect of thermal inertia in the progressive smoothing of the "Monday peak" from one week to the following.
Museo di Storia Naturale:
• Initial peak is unrealistic, consumption scale is different with respect with Scuole Poledrelli
• clear weekly pattern is absent, even if a week-size signal seems to be present, especially on the left part of the curve
• comparison of consumption and temperature curves show an inverse proportion on the long term trends
Scuole Poledrelli
0
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1000
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2000
2500
3000
3500
4000
0
2
4
6
8
10
12
14
16
18
20
Satu
rday
, 27
Dec
em
ber
Mo
nd
ay, 2
9 D
ece
mb
erW
edn
esd
ay, 3
1 D
ecem
be
rFr
iday
, 02
Jan
uar
ySu
nd
ay, 0
4 J
anu
ary
Tues
day
, 06
Jan
uar
yTh
urs
day
, 08
Jan
uar
ySa
turd
ay, 1
0 J
anu
ary
Mo
nd
ay, 1
2 J
anu
ary
Wed
nes
day
, 14
Jan
uar
yFr
iday
, 16
Jan
uar
ySu
nd
ay, 1
8 J
anu
ary
Tues
day
, 20
Jan
uar
yTh
urs
day
, 22
Jan
uar
ySa
turd
ay, 2
4 J
anu
ary
Mo
nd
ay, 2
6 J
anu
ary
Wed
nes
day
, 28
Jan
uar
yFr
iday
, 30
Jan
uar
ySu
nd
ay, 0
1 F
ebru
ary
Tues
day
, 03
Feb
ruar
yTh
urs
day
, 05
Fe
bru
ary
Satu
rday
, 07
Fe
bru
ary
Mo
nd
ay, 0
9 F
eb
ruar
yW
edn
esd
ay, 1
1 F
ebru
ary
Frid
ay, 1
3 F
eb
ruar
ySu
nd
ay, 1
5 F
ebru
ary
Tues
day
, 17
Feb
ruar
yTh
urs
day
, 19
Fe
bru
ary
Satu
rday
, 21
Fe
bru
ary
Mo
nd
ay, 2
3 F
eb
ruar
yW
edn
esd
ay, 2
5 F
ebru
ary
Frid
ay, 2
7 F
eb
ruar
ySu
nd
ay, 0
1 M
arch
Tues
day
, 03
Mar
chTh
urs
day
, 05
Mar
chSa
turd
ay, 0
7 M
arch
Mo
nd
ay, 0
9 M
arch
Wed
nes
day
, 11
Mar
chFr
iday
, 13
Mar
chSu
nd
ay, 1
5 M
arch
Tues
day
, 17
Mar
chTh
urs
day
, 19
Mar
chSa
turd
ay, 2
1 M
arch
Mo
nd
ay, 2
3 M
arch
Wed
nes
day
, 25
Mar
chFr
iday
, 27
Mar
chSu
nd
ay, 2
9 M
arch
Tues
day
, 31
Mar
chTh
urs
day
, 02
Ap
ril
Satu
rday
, 04
Ap
ril
Mo
nd
ay, 0
6 A
pri
lW
edn
esd
ay, 0
8 A
pri
lFr
iday
, 10
Ap
ril
Sun
day
, 12
Ap
ril
Tues
day
, 14
Ap
ril
kWh
T °
Temperatura Consumi
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, 27
Dec
em
ber
Mo
nd
ay, 2
9 D
ece
mb
erW
edn
esd
ay, 3
1 D
ecem
be
rFr
iday
, 02
Jan
uar
ySu
nd
ay, 0
4 J
anu
ary
Tues
day
, 06
Jan
uar
yTh
urs
day
, 08
Jan
uar
ySa
turd
ay, 1
0 J
anu
ary
Mo
nd
ay, 1
2 J
anu
ary
Wed
nes
day
, 14
Jan
uar
yFr
iday
, 16
Jan
uar
ySu
nd
ay, 1
8 J
anu
ary
Tues
day
, 20
Jan
uar
yTh
urs
day
, 22
Jan
uar
ySa
turd
ay, 2
4 J
anu
ary
Mo
nd
ay, 2
6 J
anu
ary
Wed
nes
day
, 28
Jan
uar
yFr
iday
, 30
Jan
uar
ySu
nd
ay, 0
1 F
ebru
ary
Tues
day
, 03
Feb
ruar
yTh
urs
day
, 05
Fe
bru
ary
Satu
rday
, 07
Fe
bru
ary
Mo
nd
ay, 0
9 F
eb
ruar
yW
edn
esd
ay, 1
1 F
ebru
ary
Frid
ay, 1
3 F
eb
ruar
ySu
nd
ay, 1
5 F
ebru
ary
Tues
day
, 17
Feb
ruar
yTh
urs
day
, 19
Fe
bru
ary
Satu
rday
, 21
Fe
bru
ary
Mo
nd
ay, 2
3 F
eb
ruar
yW
edn
esd
ay, 2
5 F
ebru
ary
Frid
ay, 2
7 F
eb
ruar
ySu
nd
ay, 0
1 M
arch
Tues
day
, 03
Mar
chTh
urs
day
, 05
Mar
chSa
turd
ay, 0
7 M
arch
Mo
nd
ay, 0
9 M
arch
Wed
nes
day
, 11
Mar
chFr
iday
, 13
Mar
chSu
nd
ay, 1
5 M
arch
Tues
day
, 17
Mar
chTh
urs
day
, 19
Mar
chSa
turd
ay, 2
1 M
arch
Mo
nd
ay, 2
3 M
arch
Wed
nes
day
, 25
Mar
chFr
iday
, 27
Mar
chSu
nd
ay, 2
9 M
arch
Tues
day
, 31
Mar
chTh
urs
day
, 02
Ap
ril
Satu
rday
, 04
Ap
ril
Mo
nd
ay, 0
6 A
pri
lW
edn
esd
ay, 0
8 A
pri
lFr
iday
, 10
Ap
ril
Sun
day
, 12
Ap
ril
Tues
day
, 14
Ap
ril
kWh
T °
Temperatura Consumi
Museo di Storia Naturale
Gas consumption data are measured with optical reader attached to the analogic gas meters:
• Data is gathered via radio in local concentrators that deliver them via GPRS to the pilot head-end server.
• Reading frequency is hourly but often the reading fails and the measure is postpones to the following hour.
• This is what causes the measurement jumps in the historical series.
We have analysed consumption data for one pilot building served by gas heating to verify the quality of data.
Palazzina Energia/Patrimonio:
• Impact of measurement jumps is heavy, to the point that data is scarcely useful
• Gas consumption includes also hot water preparation, as can be derived from the non-null consumption values outside of the heating season.
1.3 - Gas consumption profiles
Ufficio Energia/Patrimonio:
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0
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, 27
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day
, 30
Dec
emb
er
Frid
ay, 0
2 J
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ary
Mo
nd
ay, 0
5 J
anu
ary
Thu
rsd
ay, 0
8 J
anu
ary
Sun
day
, 11
Jan
uar
y
Wed
nes
day
, 14
Jan
uar
y
Satu
rday
, 17
Jan
uar
y
Tues
day
, 20
Jan
uar
y
Frid
ay, 2
3 J
anu
ary
Mo
nd
ay, 2
6 J
anu
ary
Thu
rsd
ay, 2
9 J
anu
ary
Sun
day
, 01
Feb
ruar
y
Wed
nes
day
, 04
Feb
ruar
y
Satu
rday
, 07
Fe
bru
ary
Tues
day
, 10
Feb
ruar
y
Frid
ay, 1
3 F
eb
ruar
y
Mo
nd
ay, 1
6 F
eb
ruar
y
Thu
rsd
ay, 1
9 F
eb
ruar
y
Sun
day
, 22
Feb
ruar
y
Wed
nes
day
, 25
Feb
ruar
y
Satu
rday
, 28
Fe
bru
ary
Tues
day
, 03
Mar
ch
Frid
ay, 0
6 M
arch
Mo
nd
ay, 0
9 M
arch
Thu
rsd
ay, 1
2 M
arch
Sun
day
, 15
Mar
ch
Wed
nes
day
, 18
Mar
ch
Satu
rday
, 21
Mar
ch
Tues
day
, 24
Mar
ch
Frid
ay, 2
7 M
arch
Mo
nd
ay, 3
0 M
arch
Thu
rsd
ay, 0
2 A
pri
l
Sun
day
, 05
Ap
ril
Wed
nes
day
, 08
Ap
ril
Satu
rday
, 11
Ap
ril
Tues
day
, 14
Ap
ril
T °
-4
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0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324
Sabato 10/01
Module 2:Test of suggestion service
The aim of this activity is to test the Heating System Suggestion service on the same two pilot buildings in Ferrara:
- Scuole Elementari Poledrelli
- Museo di Storia Naturale
The Suggestion service normally takes in input the forecasted weather condition for the following day.
However, in order to perform a test on a long baseline, for the test the suggestion service was run on an historical series of past observed data during part of the last winter season.
2.1 - The Suggestion service
The suggestion service is designed to activate on days with out-of-the-average weather.
It has been determined that on 90% of cases the absolute value of the difference between the average temperature of one day and the average temperature of the preceding day fall within 3°C for Ferrara. Days that fall outside this value are considered out-of the average.
The first plot shows the profiles of the following variables:
(temperatures on the left axis, temperature difference on the right axis)
• Maximum measured daily outside temperature (red line)
• Average measured daily outside temperature (green line)
• Minimum measured daily outside temperature (blue line)
• Out-of-the-average days (red dots)
The second plot shows the distribution of the absolute value of difference between temperature averages. The tail of the distribution is highlighted and it represents the numerosity of the out-of-the-average days.
2.2 - Service triggering
Identifying out-of-the-average days:
1
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-13
-8
-3
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Thu
rsd
ay, 0
1 J
anu
ary
Sun
day
, 04
Jan
uar
y
We
dn
esd
ay, 0
7 J
anu
ary
Satu
rday
, 10
Jan
uar
y
Tue
sday
, 13
Jan
uar
y
Frid
ay, 1
6 J
anu
ary
Mo
nd
ay, 1
9 J
anu
ary
Thu
rsd
ay, 2
2 J
anu
ary
Sun
day
, 25
Jan
uar
y
We
dn
esd
ay, 2
8 J
anu
ary
Satu
rday
, 31
Jan
uar
y
Tue
sday
, 03
Fe
bru
ary
Frid
ay, 0
6 F
eb
ruar
y
Mo
nd
ay, 0
9 F
ebru
ary
Thu
rsd
ay, 1
2 F
ebru
ary
Sun
day
, 15
Feb
ruar
y
We
dn
esd
ay, 1
8 F
ebru
ary
Satu
rday
, 21
Feb
ruar
y
Tue
sday
, 24
Fe
bru
ary
Frid
ay, 2
7 F
eb
ruar
y
Mo
nd
ay, 0
2 M
arch
Thu
rsd
ay, 0
5 M
arch
Sun
day
, 08
Mar
ch
We
dn
esd
ay, 1
1 M
arch
Satu
rday
, 14
Mar
ch
Tue
sday
, 17
Mar
ch
Frid
ay, 2
0 M
arch
Mo
nd
ay, 2
3 M
arch
Thu
rsd
ay, 2
6 M
arch
Sun
day
, 29
Mar
ch
We
dn
esd
ay, 0
1 A
pri
l
Satu
rday
, 04
Ap
ril
Tue
sday
, 07
Ap
ril
Frid
ay, 1
0 A
pri
l
Mo
nd
ay, 1
3 A
pri
l
Thu
rsd
ay, 1
6 A
pri
l
Sun
day
, 19
Ap
ril
We
dn
esd
ay, 2
2 A
pri
l
Satu
rday
, 25
Ap
ril
Tue
sday
, 28
Ap
ril
Frid
ay, 0
1 M
ay
Mo
nd
ay, 0
4 M
ay
Thu
rsd
ay, 0
7 M
ay
Sun
day
, 10
May
We
dn
esd
ay, 1
3 M
ay
Satu
rday
, 16
May
Tue
sday
, 19
May
Frid
ay, 2
2 M
ay
Mo
nd
ay, 2
5 M
ay
Thu
rsd
ay, 2
8 M
ay
Sun
day
, 31
May
T° Min T° Max T° Media Differenza
Profilo di T ° min, max e media nell’anno 2015
I giorni “anomali” sono quelli che presentano un ΔT ° > 3° tra due date continue di riferimento
Distribution of absolute values of daily average temperature differences:
Distribuzione ΔT°
0
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distribution cumulative
Secondo la curva cumulativa nel periodo di riferimento circa il 18 % dei giorni hanno un ΔT ° > 3°Questi vengono analizzati al fine di verificare l’attendibilità del servizio di suggestion, come segue :
The Simulation has then been run on a sub-portion of the span of the previous plot.
The outcome is shown in the following slide for both pilot buildings,:
• Scuole Poledrelli on the left and
• Museo di Storia Naturale on the right.
The top plots describe the heating system turn-on phase:
(hours on the left axis, temperatures on the right axis)
• Maximum measured daily outside temperature (red line)
• Minimum measured daily outside temperature (blue line)
• Suggested turn-on time (red triangles)
• Measured turn-on time (green triangles)
Bottom plots describe the heating system shutting down:
• Suggested shutting-down time (red diamonds)
• Measured shutting-down time (green diamonds)
2.3 - Suggested turn on/off times
Suggestion:
Poledrelli :
-15
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-5
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/20
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/20
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/20
15
ho
ur
of
the
day
Accensione
Estimated on
off
Measured on
off
T min
T max
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Exte
rnal
Te
mp
era
ture
[°C
]
Spegnimento
-15
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-5
0
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10
15
5
6
7
8
9
10
11
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/20
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/20
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/20
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/01
/20
15
28
/01
/20
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30
/01
/20
15
01
/02
/20
15
Exte
rnal
Te
mp
era
ture
[°C
]
-7
-2
3
8
13
12
13
14
15
16
17
18
19
20
21
Exte
rnal
Te
mp
era
ture
[°C
]
ho
ur
of
the
day
Museo :
It must be stressed that:
• measured turn-on / shut-down times have been deduced from consumption profiles with an approximation of +/- 30 minutes
• suggested turn-on / shut-down times have been computed by the suggestion service using the measured weather data for each day
• we have no way to verify if either the measured or suggested turn on/off profile succeeds in achieving the desired internal comfort profile, because we have no data describing internal temperature.
The aim of the test is instead to verify:
• how often the Suggestion service is triggered in a real scenario
• how different is the pattern of suggested turn on/off profiles with respect to what operators do out of their experience (the measured profiles)
2.3.a - Aim of the test
The analysis of the plots reveals that:
• Suggested turn-on times are very sensitive to the daily minimum temperature (and much less to the maximum), while shut-down times are almost insensitive.
• The relative dependence of suggested turn-on time with respect to external temperature throughout the days is a significative feature to compare with measured one to evaluate if the suggestion service is well tuned.
• On the contrary, it is not significative to compare the absolute values of suggested turn-on times with corresponding measured ones, because, as already pointed out, we have no way to evaluate the effectiveness in guaranteeing the required comfort of either of them.
• The same reasoning applies in principle to shut-down times, even if they do not show any relative variation throughout the days.
2.3.b - Test analysis
The suggestion service computes also the expected internal temperature profile of the building (estimated under the assumption of heating system always OFF). The profile is useful to determine whether the effect of outside temperature and solar irradiation are enough to allow a comfort level inside the building or if heating is necessary.
The two following picture apply to the two pilot building and describe:
• the estimated internal temperature (green line)
• the measured external temperature (blue line)
• the measured consumption (red line)
• suggested turn on and off times (dashed black line)
• required periods of comfort (unshaded surfaces)
2.4.a – Internal temperature profile
Suggestion:
Poledrelli : Profilo Giornaliero stimato per una data “anomala”, con ΔT° > 3°
-12
-7
-2
3
8
13
0
50
100
150
200
250
300
350
400
450
500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Co
nsu
mp
tio
nkW
h
Venerdì 16/01
Consumption Power ON T° T° Estimated
Suggestion:
Museo : Profilo Giornaliero stimato per una data “anomala”, con ΔT° > 3°
-12
-7
-2
3
8
13
0
50
100
150
200
250
300
350
400
450
500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Co
nsu
mp
tio
nkW
h
Venerdì 16/01
Consumption Power ON T° T° Estimated
Remarks:
• test day (16/01/2015) was chosen because it was one of the few out-of-the-ordinary days available in the test sample, however a more radical example should be tested.
• estimated internal temperatures do not vary a lot for the two pilot buildings.
• building thermal inertia is not considered (internal temperature of the previous day would be needed).
• building occupancy is not considered.
In the last plot of the following slide shows a comparison between
• the estimated internal temperature for a contiguous number of days
• the measured external temperature for the same span of days
It is clearly visible how the estimated internal temperature trends have no delay with respect to outside temperatures as instead you would expect due to thermal inertia of the building.
2.4.b – Internal temperature profile
Suggestion:
0
2
4
6
8
10
12
14
Thu
rsd
ay, 1
5
Frid
ay, 1
6
Satu
rday
, 17
Sun
day
, 18
Mo
nd
ay, 1
9
Tue
sday
, 20
We
dn
esd
ay, 2
1
Thu
rsd
ay, 2
2
Frid
ay, 2
3
Satu
rday
, 24
Sun
day
, 25
Mo
nd
ay, 2
6
Tue
sday
, 27
We
dn
esd
ay, 2
8
Thu
rsd
ay, 2
9
Frid
ay, 3
0
Satu
rday
, 31
Sun
day
, 01
T° Suggested T° Ext
Module 3:Suggestions andFuture activities
Suggested actions:
• Compare weather data for Ferrara coming from Sensor DB with original data from ARPA to verify if unrealistic temperature profiles derive from ingestion.
• Comparison between energy consumption for different buildings should be done after normalization with total heated surface.
• Normalization with respect to degree days should also be used if different time periods are considered.
• Correlations of consumption with irradiation and wind should be also evaluated.
Suggested test:
• keep heating on in the weekend for a couple of weeks, then turn it off in the weekends for another couple of weeks.
• do this in two different periods of Winter, at the beginning of the heating season and at its peak.
• perform the same test in buildings with different thermal inertia
• evaluate the seasonal consumption profile of the building to understand how it responds to thermal inertia and different seasonal condition and ultimately evaluate when is more efficient to keep the heating on during the weekends and when it is not.
Analysis of historical series
Suggested actions:
• Accuracy of Suggestion service will greatly benefit by adding the modelling of building's thermal inertia. This is visible in the unrealistic relation between the series of external temperatures and estimated internal temperatures that shows how the estimated internal temperature is only reacting to external temperatures and not showing any signs of thermal inertia.
• Test/validation will be more thorough if data on daily occupancy could be collected: daily registries of school canteen users should be asked to the school.
Suggested test:
• a campaign of high-frequency (e.g. 1 hour) indoor temperature measurement has been planned on 2015-2016 heating season for Scuole Elementari Poledrelli.
• two week-long campaigns: beginning of November; 3rd week of December or 2 week of January;
• during the campaigns the heating system will be set with the turn on/off profiles provided by the suggestion service.
• The absolute accuracy of the Suggestion service can be finally evaluated.
Suggestion service
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SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
Credits
For more training material and courses visit http://www.sunshineproject.eu/solutions/trainingor contact us directly at [email protected]
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Thank you!
Luca Giovannini
Sinergis Srl