Ranking appliance energy efficiency in households: Utilizing smart meter data and energy efficiency frontiers to estimate and identify the determinants of appliance energy efficiency in residential buildings

Abstract This paper offers a novel method to rank residential appliance energy efficiency utilizing energy efficiency frontiers. The method is validated using a real-world case study of 4231 buildings in Ireland. Our results show that structural factors have the largest impact on energy efficiency, followed by socioeconomic factors and behavioral factors. For example, households with high penetration of efficient lightbulbs and double-glazed windows were on average 4 and 3.5% more efficient than others. Households with the head of household having higher education are on average 1.3% more efficient than their peers. Finally, households that track their energy savings are on average 0.4% more efficient than others. Furthermore, installing heater timers, wall insulation, and living in owned residences were correlated with higher efficiency. Generally, families with kids who have full-time employment and are highly-educated are more efficient compared to families with no kids, or families with retirees or unemployed members. This result has important implications for both targeting and messaging of energy efficiency programs. Some behavioral factors demonstrated significant impact on appliance energy efficiency. For instance, households that expressed interest in making major energy-saving lifestyle changes scored higher efficiency ranks on average. Conversely, households that expressed doubt about their motivation to save energy ranked lower in efficiency. This finding validates the role of educational programs to increase awareness about energy efficiency and its importance. In short, our results show that a data-driven analysis of a population is needed to develop a balanced view of the drivers of energy efficiency, and to devise a targeted approach to improve homes’ energy efficiency.

[1]  R. Sexton,et al.  Consumer Response to Continuous-Display Electricity-Use Monitors in a Time-of-Use Pricing Experiment , 1987 .

[2]  M. Parti,et al.  The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector , 1980 .

[3]  Michael Conlon,et al.  Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study , 2012 .

[4]  Douglas W. Caves,et al.  Econometric analysis of residential time-of-use electricity pricing experiments , 1980 .

[5]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[6]  Michael Zeifman,et al.  Nonintrusive appliance load monitoring: Review and outlook , 2011, IEEE Transactions on Consumer Electronics.

[7]  R. Rajagopal,et al.  Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior , 2013 .

[8]  Ram Rajagopal,et al.  Smart Meter Driven Segmentation: What Your Consumption Says About You , 2013, IEEE Transactions on Power Systems.

[9]  Silvia Santini,et al.  Towards automatic classification of private households using electricity consumption data , 2012, BuildSys@SenSys.

[10]  Regina Lamedica,et al.  A bottom-up approach to residential load modeling , 1994 .

[11]  Silvia Santini,et al.  Automatic socio-economic classification of households using electricity consumption data , 2013, e-Energy '13.

[12]  Massimo Filippini,et al.  Short- and long-run time-of-use price elasticities in Swiss residential electricity demand , 2011 .

[13]  Ram Rajagopal,et al.  Data-Driven Benchmarking of Building Energy Efficiency Utilizing Statistical Frontier Models , 2014, J. Comput. Civ. Eng..

[14]  Lucio Soibelman,et al.  Training load monitoring algorithms on highly sub-metered home electricity consumption data , 2008 .

[15]  B. D. Hunn,et al.  The use of data envelopment analysis for evaluating building energy consumption in terms of productivity , 1998 .

[16]  Ian Paul Knight,et al.  Residential Cogeneration Systems: European and Canadian Residential Non-HVAC Electric and DHW Load Profiles For Use in Simulating the Performance of Residential Cogeneration Systems , 2007 .

[17]  Ram Rajagopal,et al.  Household Energy Consumption Segmentation Using Hourly Data , 2014, IEEE Transactions on Smart Grid.

[18]  Guy R. Newsham,et al.  The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review , 2010 .

[19]  M. Farrell The Measurement of Productive Efficiency , 1957 .

[20]  Karen Herter Residential implementation of critical-peak pricing of electricity , 2007 .