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What can we learn from high-frequency appliance-level energy metering? Results from a field experiment

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  • Chen, Victor L.
  • Delmas, Magali A.
  • Kaiser, William J.
  • Locke, Stephen L.

Abstract

This study uses high-frequency appliance-level electricity consumption data for 124 apartments over 24 months to provide a better understanding of appliance-level electricity consumption behavior. We conduct our analysis in a standardized set of apartments with similar appliances, which allows us to identify behavioral differences in electricity use. The Results show that households' estimations of appliance-level consumption are inaccurate and that they overestimate lighting use by 75% and underestimate plug-load use by 29%. We find that similar households using the same major appliances exhibit substantial variation in appliance-level electricity consumption. For example, households in the 75th percentile of HVAC usage use over four times as much electricity as a user in the 25th percentile. Additionally, we show that behavior accounts for 25–58% of this variation. Lastly, we find that replacing the existing refrigerator with a more energy-efficient model leads to overall energy savings of approximately 11%. This is equivalent to results from behavioral interventions targeting all appliances but might not be as cost effective. Our findings have important implications for behavior-based energy conservation policies.

Suggested Citation

  • Chen, Victor L. & Delmas, Magali A. & Kaiser, William J. & Locke, Stephen L., 2015. "What can we learn from high-frequency appliance-level energy metering? Results from a field experiment," Energy Policy, Elsevier, vol. 77(C), pages 164-175.
    • Handle: RePEc:eee:enepol:v:77:y:2015:i:c:p:164-175
    DOI: 10.1016/j.enpol.2014.11.021
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    2. Asensio, Omar Isaac & Delmas, Magali A., 2016. "The dynamics of behavior change: Evidence from energy conservation," Journal of Economic Behavior & Organization, Elsevier, vol. 126(PA), pages 196-212.
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    4. Omar Alrawi & I. Safak Bayram & Sami G. Al-Ghamdi & Muammer Koc, 2019. "High-Resolution Household Load Profiling and Evaluation of Rooftop PV Systems in Selected Houses in Qatar," Energies, MDPI, vol. 12(20), pages 1-25, October.
    5. Weber, Sylvain & Puddu, Stefano & Pacheco, Diana, 2017. "Move it! How an electric contest motivates households to shift their load profile," Energy Economics, Elsevier, vol. 68(C), pages 255-270.
    6. Louis-Gaëtan Giraudet, 2018. "Energy efficiency as a credence good: A review of informational barriers to building energy savings," Policy Papers 2018.04, FAERE - French Association of Environmental and Resource Economists.
    7. Chen, Victor L. & Delmas, Magali A. & Locke, Stephen L. & Singh, Amarjeet, 2017. "Information strategies for energy conservation: A field experiment in India," Energy Economics, Elsevier, vol. 68(C), pages 215-227.
    8. Frondel, Manuel & Sommer, Stephan & Vance, Colin, 2019. "Heterogeneity in German Residential Electricity Consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 131(C), pages 370-379.
    9. Lynham, John & Nitta, Kohei & Saijo, Tatsuyoshi & Tarui, Nori, 2016. "Why does real-time information reduce energy consumption?," Energy Economics, Elsevier, vol. 54(C), pages 173-181.
    10. Giraudet, Louis-Gaëtan, 2020. "Energy efficiency as a credence good: A review of informational barriers to energy savings in the building sector," Energy Economics, Elsevier, vol. 87(C).
    11. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    12. Farrow, Katherine & Grolleau, Gilles & Mzoughi, Naoufel, 2018. "Less is more in energy conservation and efficiency messaging," Energy Policy, Elsevier, vol. 122(C), pages 1-6.
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