II Workshop Residential consumption and smart consumer 26 November 2013

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1 II Workshop Residential consumption and smart consumer 26 November 213

2 B A C K G R O U N D RESIDENTIAL CONSUMPTION AND SMART CONSUMER

3 ENERGY CONSUMPTION IN RESIDENTIAL BUILDINGS BUILDING PERFORMACE REAL USES Building systems Occupation and maintenance Building envelope External climates Indoor comfort Occupant behaviour Economical/ Social aspects Crucial aspects influencing real energy consumption in buildings

4 Numero di case [%] II Workshop ENERGY CONSUMPTION IN RESIDENTIAL BUILDINGS: occupant behaviour 4% Building sector Energy consumption in Europe times Monitoring of 29 [identical] dwellings in Denmark Salubrtity Energy Consumption [kwh/m²anno] Comfort level Energy consumption Weakness in replicating dynamics governing real uses in buildings Energy consumption may vary up to 15%

5 efficiency II Workshop I CONSUMI ENERGETICI IN AMBITO RESIDENZIALE: il sistema casa Technical limitations White goods reached asymptote in energy efficiency Occupant behaviour is new driver for the enhancement of energy efficiency at home time White goods Occupant behaviour Effect of awareness campaing and smart monitoring on energy consumption -15%* *mean value accordingly to literature studies

6 A N A L Y S I S M E T H O D S ENERGY CONSUMPTION AND SMART CONSUMER

7 THE BIG QUESTION how about the amount of energy we consume in our homes?

8 THE BIG ANSWER Improve occupant behaviour by means a raise in awareness in energy consumption at home. Usage of real-time monitoring systems and persuasive communication campaigns Tiziana Buso, Simona D Oca, Theodora Karagiannidou 8

9 A useful tool for users Monitoring typical energy consumption at home Real time visualisation of electrical loads Predicting future energy consumption, based on typical pattern of usage of appliances and white goods Compairing domestic energy consumption to similar user type (familty type) to induce and stimulate users into social-economical competition towards energy-savings profiles.

10 3. Compairing energy consumption of similar family type 1. Define user type 2. Real-time monitoring of electricty consumption

11 1 User Profiling Interview web-questionmaire - House type/organisation - Type of equipments/white goods - Household composition Study of the influencing factor Identification of typical user profiles

12 2 Smart Monitoring - smart meter (Enel) - smart appliance (washing machine Indesit) - smart plug (dishwasher, TV, refrigerator)

13 3 Persuasive Communication Energetic Feedback 1- real-time monitoring of electricity consumption in 31 trial homes 2- real-time visualisation of energy load profiles (hourly, daily, weekly based) 3- idetification of KPIs KEY PERFORMANCE INDICATORS 4- tailored feedback

14 R E S U L T S ENERGY CONSUMPTION AND SMART CONSUMER

15 Monitoring of 31 trial homes Data analysis from real-time monitoring Analysis of annual consumption trends, energy consumption breakdown Data analysis from real-time monitoring KEY PERFORMANCE INDICATORS (KPIs) Analysis of the effect of newsletters on energy consumption Evaluation of seasonal impacts Analysis of global energy consumption Comparative analysis among user types Analisis of best practise user s behabiour Evaluation of the effect of energy feedback and tailored information

16 kwh/anno II Workshop 1 Analysis of annual consumption trends, energy consumption breakdown La tua suddivisione dei consumi elettrici La tua proiezione di consumo annuale comparata con quella di famiglie a te simili e rispetto alla media nazionale (fonte AEEG) TV 12% Pc 2% Frigorifero 12% TU 5 Lavapiatti 6% Lavatrice 3% Altro consumo 65% kwh/anno media italiana

17 /anno II Workshop kwh/anno 1 Analysis of annual consumption trends, energy consumption breakdown 16 La tua proiezione di bolletta annuale comparata con quella di famiglie a te simili e rispetto alla media nazionale (fonte ISTAT) 25 La tua proiezione di consumo annuale pro capite comparata con quella di famiglie a te simili e rispetto alla media nazionale (fonte ISTAT) TU 2 TU /anno media italiana kwh/anno media italiana 4 5 2

18 2 KEY PERFORMANCE INDICATORS (KPIs) 1. QUANTITATIVE Can be presented with a number kwh/yr /yr 2. QUALITATIVE Can t be presented with a number Energy behavioural profile PROCESS LEADING Efficiency functionning Energy consumption prediction % reduction in energy consumption Dinamic calibrated schedules

19 Wh 1st week 2nd week 3rd week 4th week 5th week 6th week 7th week II Workshop 8th week 9th week 1th week 11th week 12th week 13th week 14th week 2 1. QUANTITATIVE KPIs INTENSIVE AVERAGE SAVING

20 W Wh Wh W II Workshop 2 2. QUALITATIVE KPIs WEEK DAYS average daily pattern load Hourof the day AVERAGE DAILY PATTERN LOAD weekday household consumption 2 households family type Monday Tuesday Wednesday Thursday Friday mean value Hour of the day Hour of the day Hag u1 111 Hag u2 114 Hag u3 115 Hag u4 116 Hag u5117 Hag u1 111 Hag u2 114 Hag u3115 Hag u4116 Hag u WEEK END average daily pattern load Hourof the day AVERAGE DAILY PATTERN LOAD weekend household consumption 2 households family type Saturday Sunday mean value Definition of average daily electrical pattern load for each trial-user For each of the household-familytype monitored trialists, an average 24 hour pattern load indentifying household typical behaviour regarding global electric consumption (smart meter) was created 2.Family type profiling Daily mean electric consumption for family type were created. Typical electrical consumption corresponds to the daily average load curve of families having same composition

21 W W II Workshop 2 2. QUALITATIVE KPIs WEEK DAYS Electricity PMC Hour of the day mean value energy intensive energy saver energy saver threshold energy intensive threshold WEEK END Electricity PMC Hour of the day mean value energy intensive energy saver energy saver threshold energy intensive threshold BAR BAR 3. Behavioural pattern profiling Creation of average load profiles here named Predicted Mean Consumption (PMC) for electricity consumption. For every family type, threshold values were settled around the average Predicted Mean Consumption (PMC), based on different behavioural pattern ranging from energy saver, on the average, up to energy intensive for that specific family type. Not a singular value, but a range of positive and negative values around the average were consider as acceptable for a predictable family type consumption. Specifically, a Buffer Acceptability Range (BAR) composed by three bands was created.

22 w w II Workshop 2 2. QUALITATIVE KPIs Average Daily Electricity Consumption and 2 Households PMC WEEK DAYS Hour of the day mean value energy intensive energy saver energy saver threshold energy intensive threshold Hag trialist 117 Average Daily Electricity Consumption and 2 HouseholdsPMC WEEK END Hour of the day mean value energy intensive energy saver energy saver threshold energy intensive threshold Hag trialist Comparison of daily pattern loads to PMC Predicted Mean Consumption was used as self reference value (horizontal benchmark) for the average electricity consumption of each specific family type. Daily average pattern loads of trialists were compared to the PMC of their belonging family type, 5. Evaluation of percentage of time in a BAR By comparing daily pattern loads of singular trialists with PMC of the belonging family type, it became possible to highlight for how many hours during weekdays and weekend the electric consumption of each trialist was targeted as energy saver, on the average, up to energy intensive,

23 2 2. QUALITATIVE KPIs Electricity Consumption Percentage of time in a BAR during WEEK DAYS Electricity Consumption Percentage of time in a BAR during WEEK END 1% 1% 63% 58% 36% 41% % 2% 4% 6% 8% 1% % 2% 4% 6% 8% 1% Energy Intensive Average Energy Saver Energy Intensive Average Energy Saver

24 2 2. QUALITATIVE KPIs TU Percentuale di tempo in cui il tuo consumo è associabile ad un profilo d uso energetico * *fasce di valore medio relativo al campione di trialisti del sistema profilo risparmio energetico 88% 5% 67% 88% 29% 82% 17% 25% 88% 42% 21% 2% profilo consumo medio 12% 29% 17% 12% 17% 8% 67% 5% 12% 33% 21% 38% profilo consumo intenso 21% 17% 54% 8% 17% 25% 25% 58% 42%

25 [ W) [ W) [ W) [ W) [ W) [ W) [ W) II Workshop [>6184 W] [ W) [ W) [ W) [ W) [ W) [>84 W) 2 3. PROCESS KPIs Analysis of the effect of newsletters on energy consumption Evaluation of seasonal impacts Trialists with 4.5 kw Contractual Power Percentage of usage of the contractual power Trialists with 6 kw Contractual Power Percentage of usage of the contractual power,12%,1%,8%,6%,4%,2%,%,11%,8%,4%,2%,2%,%,%,%,2%,18%,16%,14%,12%,1%,8%,6%,4%,2%,%,18%,13%,8%,1%,1%,%

26 2 7% Analisi Dati mointoraggio real-time sistema 3. KPIs PROCESSO Analisi consumi stand-by utenti analisi percentuale di incidenza su consumi globali Influence of standby power on global electricity energy consumption 6% 5% 4% 3% 2% 1% % Trialists % STANDBY TOTALE % STANDBY TV

27 2 3. PROCESS KPIs Energy consumption per hourly tariff 9% 8% 7% YOU Economical Hourly Rate Category based on National Italian Standards Category F1 mon fry % 5% 4% Category F2 mon fry % 2% 1% Category F3 weekend/ holydays %

28 [Wh] II Workshop 3 Analysis of the effect of newsletters on energy consumption 9 web-newsletters (via ) in the period from 27/3/213 to 17/7/ Daily saved energy per newsletter BEST PRACTICE SAVED Kg CO /3/213 21/5/213 5/4/213 23/4/213 1/3/213 15/2/213 9/7/213 15/3/213 17/7/213 Newsletters' date

29 27/3/213 21/5/213 5/4/213 23/4/213 1/3/213 9/7/213 15/3/213 17/7/213 15/2/213 27/3/213 21/5/213 5/4/213 23/4/213 1/3/213 9/7/213 15/3/213 17/7/213 15/2/213 3 II Workshop Analysis of the effect of newsletters on energy consumption 9 web-newsletters (via ) in the period from 27/3/213 to 17/7/ Global savings per Newsletter 6 Global kg CO2 savings per Newsletter

30 [Wh] II Workshop 3 Analysis of the effect of newsletters on energy consumption 9 web-newsletters (via ) in the period from 27/3/213 to 17/7/ Daily saved energy per trialist BEST PRACTICE SAVED Kg CO2 17/7/213 9/7/213 21/5/213 23/4/213 5/4/213 27/3/213 15/3/213 1/3/213 15/2/213 Newsletters' date

31 II Workshop kg CO2 3 Analysis of the effect of newsletters on energy consumption 9 web-newsletters (via ) in the period from 27/3/213 to 17/7/ Global savings per Trialist Global kg CO2 savings per Trialist newsletter savings Kg CO

32 kwh/year Hag 134 Hag 123 Hag 14 Hag 144 Hag 133 Hag 137 Hag 126 Hag 128 Hag 129 Hag 141 Hag 118 Hag 12 Hag 122 II Workshop Hag 11 Hag 12 Hag 115 Hag 13 Hag 152 Hag 125 Hag 113 Hag 132 Hag 135 Hag 18 Hag 119 Hag 7 Hag 11 Hag 121 Hag 116 Hag 138 Hag 114 Hag 117 Hag 27 Hag 127 Hag Analysis of global energy trends 9 ANNUAL ENERGY CONSUMPTION PROJECTION

33 /year Hag 134 Hag 123 Hag 14 Hag 144 Hag 133 Hag 137 Hag 126 Hag 128 Hag 129 Hag 141 Hag 118 Hag 12 Hag 122 II Workshop Hag 11 Hag 12 Hag 115 Hag 13 Hag 152 Hag 125 Hag 113 Hag 132 Hag 135 Hag 18 Hag 119 Hag 7 Hag 11 Hag 121 Hag 116 Hag 138 Hag 114 Hag 117 Hag 27 Hag 127 Hag Analysis of global energy trends 16 ANNUAL ENERGY BILL PROJECTION

34 kwh/year Hag 123 Hag 134 Hag 129 Hag 115 Hag 14 Hag 141 Hag 118 Hag 122 Hag 11 Hag 12 Hag 116 Hag 125 II Workshop Hag 114 Hag 117 Hag 132 Hag 128 Hag 18 Hag 127 Hag 133 Hag 137 Hag 13 Hag 126 Hag 12 Hag 135 Hag 138 Hag 119 Hag 113 Hag 136 Hag 121 Hag 27 4 Analysis of global energy trends 4 ANNUAL ENERGY CONSUMPTION PROJECTION procapite

35 4 Analysis of global energy savings -6% -5% -4% -3% -2% -1% % 1% 2% 3% 4% 5% 6% 73% Trialists reduced Energy consumption BEST PRACTICE: A trialist reduced energy consumption up to 37% Trialists reduced energy consumption On the average18% 33% Trialists not reduced energy consumption

36 Wh 1/1/213 15/1/213 29/1/213 12/2/213 II Workshop 26/2/213 12/3/213 26/3/213 9/4/213 23/4/213 7/5/213 21/5/213 4/6/213 18/6/213 2/7/213 16/7/213 3/7/213 13/8/213 27/8/213 1/9/213 24/9/213 5 Analysis of «best practise» energy related behaviour BEST PRACTICE: -37% energy consumption Hag xxx Fallback effect the phenomenon in which newness of a change causes people to react, but then the reaction diminishes and the newness wears off. weeks of monitoring period Wilhite and Ling, 1999

37 5 Analysis of «best practise» energy related behaviour Feedback from trialist "Ritengo utili soprattutto le informazioni relative al consumo globale della settimana, per controllare l'andamento dei consumi. Sarebbe interessante, se disponibile anche il consumo globale relativo alla singola presa monitorata per eventualmente intervenire per adottare misure reletive ad un contenimento dei consumi.posso dire che fin da ora dopo aver visto l'assorbimento del computer non lo lascio più acceso quando non lo utilizzo. "Per diminuire il consumo in stand by mi ripropongo di inserire un interruttore sulla presa di alimentazione della TV e togliere le luci di cortesia a led che ho nel corridoio, per poi verificare la diminuzione. Noto con soddisfazione che il mio frigorifero ha il consumo più basso tra gli sperimentatori, come classe è A+.

38 5 Analysis of «best practise» energy related behaviour "Dal terzo grafico risulta che uso soprattutto la lavatrice in fascia F1, al riguardo ho sensibilizzato mia moglie, anche mediante la dimostrazione dei grafici di evitare il più possibile l'uso della lavatrice nella fascia F1.Abbiamo il problema che la stanza dove è posta la lavatrice è vicina alle camere e il funzionamento durante la notte disturba. "Ho mostrato il grafico a mia moglie, lei dice che è costretta ad usare cicli ad alta temperatura per i "" bianchi "" perchè altrimenti rimangono macchiati ( ad esempio le tovaglie), pur usando il Dash. " Penso che una parte sostanziale della diminuzione dei consumi sia dovuta ad un fattore stagionale perchè in inverno uso spesso due deumidificatori portatili. Come comportamento virtuoso mia moglie dice che cerca di usare meno spesso la lavatrice sfruttando ogni singolo lavaggio a pieno carico e magari questo potrebbe essere verificato dai vostri report.

39 6 Conclusions: understanding the human factor Measure occupant behaviour Avoid energy wastes Little changes in consumer behaviour Boost occupant energy saving profiles Low capital investment VS High potential of apllicability Energy related behaviour VS social-economical sphere

40 Thanks for the attention questions?

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