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![Suggestion:
Poledrelli :
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Museo :](https://image.slidesharecdn.com/s24-validationactivitieforscenario2caseferrara-160210151105/85/S-2-4-Validation-Activities-for-Scenario-2-case-Ferrara-17-320.jpg)
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S.2.4 Validation Activities for Scenario 2 (case Ferrara)
The document analyzes historical consumption and weather data from two buildings heated by district heating in Ferrara, Italy. It examines daily and seasonal profiles of consumption, temperature, and heating system usage. It also tests a heating system suggestion service that predicts optimal heating turn-on/off times using weather forecasts, evaluating its performance on past data from the two buildings. The analysis found inverse relationships between consumption and temperature as expected, and that the suggestion service accurately predicted turn-on/off times for most out-of-average temperature days.
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S.2.4 Validation Activities for Scenario 2 (case Ferrara)
- 1. D6.4 S1.4 Validation activitiesfor Scenario 2 – case Ferrara
- 2. Module 1: Analysis ofhistorical series of consumption and weather data
- 3. 1.1.a - Dailyprofiles 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
- 4. 1.1.b - Dailyprofiles 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.
- 5. -4 1 6 11 0 50 100 150 200 250 300 0 1 23 4 5 6 7 8 9 101112131415161718192021222324 -4 1 6 11 0 50 100 150 200 250 300 0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324 -4 1 6 11 0 50 100 150 200 250 300 0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324 -4 1 6 11 0 50 100 150 200 250 300 0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324 Scuole Poledrelli Museo di Storia Naturale Martedì 13/01 Mercoledì 14/01
- 6. 1.2 - Seasonalprofiles 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
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- 9. Gas consumption dataare 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
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- 11. Module 2: Test ofsuggestion service
- 12. The aim ofthis 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
- 13. The suggestion serviceis 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
- 14. Identifying out-of-the-average days: 1 2 3 4 5 6 7 8 9 -13 -8 -3 2 7 12 17 22 27 32 Thursday,01January Sunday,04January Wednesday,07January Saturday,10January Tuesday,13January Friday,16January Monday,19January Thursday,22January Sunday,25January Wednesday,28January Saturday,31January Tuesday,03February Friday,06February Monday,09February Thursday,12February Sunday,15February Wednesday,18February Saturday,21February Tuesday,24February Friday,27February Monday,02March Thursday,05March Sunday,08March Wednesday,11March Saturday,14March Tuesday,17March Friday,20March Monday,23March Thursday,26March Sunday,29March Wednesday,01April Saturday,04April Tuesday,07April Friday,10April Monday,13April Thursday,16April Sunday,19April Wednesday,22April Saturday,25April Tuesday,28April Friday,01May Monday,04May Thursday,07May Sunday,10May Wednesday,13May Saturday,16May Tuesday,19May Friday,22May Monday,25May Thursday,28May Sunday,31May 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
- 15. Distribution of absolutevalues of daily average temperature differences: Distribuzione ΔT° 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 5 10 15 20 25 30 35 40 0 1 2 3 4 5 6 7 8 9 10 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 :
- 16. The Simulation hasthen 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
- 17. Suggestion: Poledrelli : -15 -10 -5 0 5 10 15 0 2 4 6 8 10 12 14/01/2015 16/01/2015 18/01/2015 20/01/2015 22/01/2015 24/01/2015 26/01/2015 28/01/2015 30/01/2015 01/02/2015 houroftheday Accensione Estimated on off Measuredon off T min T max -7 -2 3 8 13 12 13 14 15 16 17 18 19 20 21 ExternalTemperature[°C] Spegnimento -15 -10 -5 0 5 10 15 5 6 7 8 9 10 11 12 14/01/2015 16/01/2015 18/01/2015 20/01/2015 22/01/2015 24/01/2015 26/01/2015 28/01/2015 30/01/2015 01/02/2015 ExternalTemperature[°C] -7 -2 3 8 13 12 13 14 15 16 17 18 19 20 21 ExternalTemperature[°C] houroftheday Museo :
- 18. It must bestressed 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
- 19. The analysis ofthe 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
- 20. The suggestion servicecomputes 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
- 21. Suggestion: Poledrelli : ProfiloGiornaliero 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 ConsumptionkWh Venerdì 16/01 Consumption Power ON T° T° Estimated
- 22. Suggestion: Museo : ProfiloGiornaliero 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 ConsumptionkWh Venerdì 16/01 Consumption Power ON T° T° Estimated
- 23. 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
- 24.
- 25. Module 3: Suggestions and Futureactivities
- 26. Suggested actions: • Compareweather 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
- 27. Suggested actions: • Accuracyof 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
- 28. www.sunshineproject.eu SUNSHINE - SmartUrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) Credits For more training material and courses visit http://www.sunshineproject.eu/solutions/training or contact us directly at training@sunshineproject.eu Source:www.unionegeometri.com Thank you! Luca Giovannini Sinergis Srl luca.giovannini@sinergis.it