A Method to Disaggregate Structural and Behavioral Determinants of Residential Electricity Consumption Stanford ARPA-E Buildings Research Amir Kavousian, PhD Student Stanford University 1
Outline Background and motivation model of data and preprocessing efforts 2
Purpose: quantify the contribution of individual electricity consumption determinants. Introduction: practical questions 3
Purpose: inform short-, medium-, and long-term energy efficiency plans. Short-term efforts Medium-term efforts Long-term efforts Always on (idle) 4
Outline Background and motivation model of data and preprocessing efforts 5
Distinguish between idle consumption of the house ( always on loads) and peak consumption 6
Factor categorization Nature of the determinant Time Span of Modification Effort and Persistence 7
Factor categorization Daily maximum Daily minimum Short-term Mediumterm Long-term Outside the scope of influence Weather and Location Physical characteristics of the building Appliance and electronics stock Occupancy and occupants behavior model Daily habits Decisions that have medium or long-term effects Occupancy 8
Distinguish between determinants affected by floor area and the rest Assign different weights for variables affected by floor area; 9
Outline Background and motivation model of data and preprocessing efforts 10
Google Powermeter Home electricity Consumption monitoring 11
Experiment design Ten-minute interval smart meter data for 1628 households From February 28, 2010 through October 23, 2010 (238 days) CDD HDD 12
Parameters: 114 Survey Questions in Total, Broken Down into 5 Major and 12 Minor Categories (I) External Factors 1- Climate and Geography (6) (II) Building Design and 2- Building (5) Construction 3- Home Improvements (12) (III) Building Systems and 3- Fuel Use (6) Appliances 4- Appliances (14) 5- Occupants (12) 6- Energy Efficiency Habits (14) 7- Payment items, method, estimate, feedback (6) 8- How informed about appliance usage (5) (IV) Occupants 9- Level (17) 10- Effort to learn EE measures (7) 11- Personal Info of the Survey Respondent (6) 12- Thermostat Setpoint (4) Weather and Location House Appliances and Electronics 13 Occupants
analysis and modeling summary Factor analysis for behavioral factors Forward stepwise regression model on explanatory factors Ranks variables based on their importance Is easy to interpret: sequentially improving the model, one variable at a time. 14
Outline Background and motivation model of data and preprocessing efforts 15
Electricity consumption in summer * is significantly determined by weather (AC load) Weather and Location House Appliances and Electronics Occupants Variance explained Average of cooling degree day values (38%) Nb of refrigerators (4%) Climate zone (3%) Type of Building (2%) Nb of freezers (2%) Nb of entertain t devices except TV s (2%) Thermostat setpoint habits (2%) excludes Zip Code and Floor Area 16
Electricity consumption in winter * is mostly affected by occupant behavior and energy-intensive appliances Weather and Location House Appliances and Electronics Occupants Variance explained Turning lights off when not using (20%) Nb of refrigerators (6%) Elec. Water Heater (5%) Pet ownership (4%) to address global warming (3%) Nb of occupants (Sq. rt) (2%) Purchasing energy-start appl. & elec. (2%) * excludes Zip Code and Floor Area 17
Daily minimum consumption in summer is significantly affected by highconsumption appliances Weather and Location House Appliances and Electronics Occupants Variance explained Average of cooling degree day values (26%) Nb Refriger ators (7%) Nb of non-tv entertain t devices (3%) Pet ownership (2%) Thermostat setpoint habits (2%) Purchasing energy-start appl. & elec. (2%) 18
Daily minimum consumption in winter is significantly affected by highconsumption appliances Weather and Location House Appliances and Electronics Occupants Variance explained Turning lights off when not using (19%) Nb of Refrig. (7%) Nb of entertain t devices except TV s (4%) Purchase of energy-star appliances and elec. (3%) Water heater usage behavior (2%) to reduce consumption to address global warming (2%) 19
Daily maximum model for summer * is mostly affected by occupants utilization of high-consumption end uses Weather and Location House Appliances and Electronics Occupants Variance explained Average of cooling degree day values (31%) Elec. water heater (4%) Elec. clothes dryers (4%) Nb of freezers (4%) Nb of refrigerators (4%) Nb of occupants (sqrt) (2%) * excludes Zip Code and Floor Area 20
Daily maximum consumption in winter * is mostly affected by occupant behavior and energy-intensive activities Weather and Location House Appliances and Electronics Occupants Variance explained Turning lights off when not using (13%) Elec. Water heater (11%) Nb of occupants (4%) Pet ownership (4%) Elec. clothes dryers (3%) Nb of refrigerators (3%) excludes Zip Code and Floor Area 21
Behaviors with long-term short-term Nb Outside Lights Pet of Ownership Refrigerators Off Temperature of Behavior models impact Variance explained Average of cooling degree day values (38%) Weather and Location House Appliances and Electronics Occupants Ave summer Turning lights off when not using (20%) Ave winter Average of cooling degree day values (26%) Nb Refriger ators (7%) Min summer Turning lights off when not using (19%) Nb of Refrig. (7%) Min winter Average of cooling degree day values (31%) Max summer Turning lights off when not using (13%) Elec. Water heater (11%) Max winter 22
Thank you! 23