Effectof OccupantBehaviorin Energy Operationof Buildings

Transcription

1 Seminar 10 Rehva Seminar ASHRAE WINTER MEETING 2014 Effectof OccupantBehaviorin Energy Operationof Buildings REHVA Seminar: Operation of Energy Efficient Buildings Stefano Paolo Corgnati Department of Energy, Politecnico di Torino (I) Rehva Vice-President

2 MAGNITUDE OF THE PROBLEM ENERGY CONSUMPTION FOR SPACE HEATING IN 290 "IDENTICAL " DWELLINGS in DENMARK THE HIGHEST ENERGY CONSUMPTION ARE 3 TIMES HIGHER THAN THE LOWEST ONES

3 INFLUENCING FACTORS ON ENERGY CONSUMPTIONS IEA-ECBCS Annex 53 CLIMATE BUILDING ENVELOPE HVAC I.E.Q. OCCUPANT BEHAVIOUR OPERATION & MANAGEMENT Designed building energy performances Real building energy consumption IEA-EBC Annex 66 Reducing GAP between actual and predicted building energy consumptions Users Education and Engagement

4 PRINCIPLES of OB ENERGY RELATED ACTIONS Occupant s stimolus and reactions - Indoor DRIVERS Psychological parameters ACTIONS & ACTION SCENARIOS Physical parameters - IAQ - Outdoor temperature temperature - Rain - Wind -Season -.. Possible actions (combinations and sequences) -Window opening/closing - Use of thermostat/manual valves -Clothes changes -. Contextual parameters -Season - Time of the day -.. Physiological parameters Social parameters Action aimed at improving IEQ and/or reducing EC with respect to the previous perceived status PARAMETERS VARIATIONS -Variation on the air change rate -Variation on clothing thermal resistance - IEQ ENERGY CONSUMPTIONS

5 PROCEDURE for OB ADVANCED INPUT MODELs in ENERGY DYNAMIC SIMULATIONS kwh/m Behaviours I DRIVERS COLLECTION PROBABILISTIC OUTPUT V Logistic regression, Markov Chains. N TIMES II STATISTICAL ANALYSIS SIMULATIONS IV IMPLEMENTATION III Probabilistic approach

6 DEVELOPMENT NEW OB ADVANCED INPUT MODELs Measurements Outdoor Unit Indoor Unit Air temperature C Temperature C CO 2 concentration ppm Relative humidity % Surveys DRIVERS COLLECTION: INPUT for STATISTICAL ANALYSIS DRIVERS IDENTIFICATION: OUTPUT of STATISTICAL ANALYSIS & INPUT for AIMs WINDOW OPENING TURNING UP HEATING SET-POINT Drivers Period of the day Drivers Period of the day Indoor temperature Indoor relative humidity Outdoor temperature Outdoor temperature Indoor relative humidity Solar hours

7 APPLICATION OF OB AIMs IN ENERGY DYNAMIC SIMULATIONS SCHEDULES SET POINTS BUILDING ENERGY SIMULATION TOOL DETERMINISTIC Result PROBABILISTIC input Window opening, Thermostatadjustments, Lighting adjustments, PROBABILITY DISTRIBUTION of building energy performance Frequency Algorithm

8 BEHAVIOURAL VALIDATION 1.VALIDATION THROUGH STATISTICS 2. VALIDATION THROUGH DYNAMIC SIMULATION Frequency Frequency PROBABILISTIC Temperature [ C] MEASUREMENTS DETERMINISTIC Temperature [ C] MEASUREMENTS

9 STIMULI TO THE USERS User education program Feedback about user s energy behaviour 1. real-time monitoring of electrical consumption (31 trials) 2. real-time visualisation of energy consumption, profile and tendencies (hour/day/week) KPI 3. Definizione and comparison of KEY PERFORMANCE INDICATORS 4. Feedbacks and advices specifically addressed to each single user

10 STIMULI TO THE USERS Global savings -40% -30% -20% -10% 0% 10% 20% 30% 40% % Hag % of the users decresed their consumptions Best practice: 40% reduction Average: 9% reduction -33% -18% -15% -14% -8% -8% -6% ENERGY SAVING POTENTIAL for a basis of 29.2 Million users and 61.3 TWh of annual domestic electricty consumption in Italy (AEEG, 2013) (corresponding to -9% saving) TWh Million /customer Million T CO 2-1 Million Tep Million the value in white certificates 125-4% 113-1% % 11% 11%

11 Significant GAP between predicted and actual energy consumption Need of ADVANCED INPUT MODELING for OB Innovation in modelling multiple OB profile and need of standardized probabilistic OB inputs for building energy calculation for ADVANCED ROBUST BUILDING DESIGN High potentiality to reduce actual energy consumption through USERS SMART METERING for SMART USERS TODAY 3.84 kwh THIS WEEK kwh Stefano Paolo Corgnati Department of Energy, Politecnico di Torino (I) Rehva Vice-President

12 BIBLIOGRAPHY 1. H. Polinder, M. Schweiker, A. van der Aa, K. Schakib-Ekbatan, V. Fabi, R. Andersen, N. Morishita, C. Wang, S. Corgnati, P. Heiselberg, Da Yan, B. Olesen, T. Bednar, A. Wagner. Total energy use in buildings. Analysis and evaluation methods. Final report Annex 53. Vol II: Occupant behavior and modelling Published by Institute for Building Environment and Energy Conservation, Tokyo, Japan. ISBN: pp V. Fabi, R.V Andersen, S.P. Corgnati, B.W. Olesen, Occupants' window opening behaviour: A literature review of factors influencing occupant behavior and models, Building Environment 2012, Vol. 58, pp , ISSN: , DOI: /j.buildenv V. Fabi, R.V. Andersen, S.P. Corgnati, B.W. Olesen, A methodology for modelling energy-related human behaviour: Application to window opening behaviour in residential buildings, Building Simulation 2013, Vol. 6, Issue 4, pp , ISSN: R.V. Andersen, V. Fabi, J. Toftum, S.P. Corgnati, B. W. Olesen, Window opening behaviour modelled from measurements in Danish dwellings, Building and Environment 2013, Vol. 69, pp , ISSN: V. Fabi, R.V. Andersen, S.P. Corgnati, Influence of occupant s heating set-point preferences on indoor environmental quality and heating demand in residential buildings, HVAC&R Research Journal 2013, Vol. 19, Issue 5, pp , ISSN DOI: / D Oca S., Corgnati S.P., Fabi V., Andersen R.K., Effect of thermostat and window opening occupant behavior models on energy use in homes, Building Simulation 2014, ISSN: , DOI: /s , vol. 7, issue 3, pp. 1-12