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Revealing household characteristics from smart meter data

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  • Beckel, Christian
  • Sadamori, Leyna
  • Staake, Thorsten
  • Santini, Silvia

Abstract

Utilities are currently deploying smart electricity meters in millions of households worldwide to collect fine-grained electricity consumption data. We present an approach to automatically analyzing this data to enable personalized and scalable energy efficiency programs for private households. In particular, we develop and evaluate a system that uses supervised machine learning techniques to automatically estimate specific “characteristics” of a household from its electricity consumption. The characteristics are related to a household's socio-economic status, its dwelling, or its appliance stock. We evaluate our approach by analyzing smart meter data collected from 4232 households in Ireland at a 30-min granularity over a period of 1.5 years. Our analysis shows that revealing characteristics from smart meter data is feasible, as our method achieves an accuracy of more than 70% over all households for many of the characteristics and even exceeds 80% for some of the characteristics. The findings are applicable to all smart metering systems without making changes to the measurement infrastructure. The inferred knowledge paves the way for targeted energy efficiency programs and other services that benefit from improved customer insights. On the basis of these promising results, the paper discusses the potential for utilities as well as policy and privacy implications.

Suggested Citation

  • Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    • Handle: RePEc:eee:energy:v:78:y:2014:i:c:p:397-410
    DOI: 10.1016/j.energy.2014.10.025
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    References listed on IDEAS

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    1. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    2. Vasconcelos & Jorge, 2008. "Survey of Regulatory and Technological Developments Concerning Smart Metering in the European Union Electricity Market," EUI-RSCAS Working Papers 1, European University Institute (EUI), Robert Schuman Centre of Advanced Studies (RSCAS).
    3. Räsänen, Teemu & Ruuskanen, Juhani & Kolehmainen, Mikko, 2008. "Reducing energy consumption by using self-organizing maps to create more personalized electricity use information," Applied Energy, Elsevier, vol. 85(9), pages 830-840, September.
    4. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9-10), pages 1082-1095, October.
    5. Sousa, José Luís & Martins, António Gomes & Jorge, Humberto, 2013. "Dealing with the paradox of energy efficiency promotion by electric utilities," Energy, Elsevier, vol. 57(C), pages 251-258.
    6. Poortinga, Wouter & Steg, Linda & Vlek, Charles & Wiersma, Gerwin, 2003. "Household preferences for energy-saving measures: A conjoint analysis," Journal of Economic Psychology, Elsevier, vol. 24(1), pages 49-64, February.
    7. Noah J. Goldstein & Robert B. Cialdini & Vladas Griskevicius, 2008. "A Room with a Viewpoint: Using Social Norms to Motivate Environmental Conservation in Hotels," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 35(3), pages 472-482, March.
    8. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    9. Ian Ayres & Sophie Raseman & Alice Shih, 2009. "Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage," NBER Working Papers 15386, National Bureau of Economic Research, Inc.
    10. Bertoldi, Paolo & Rezessy, Silvia & Lees, Eoin & Baudry, Paul & Jeandel, Alexandre & Labanca, Nicola, 2010. "Energy supplier obligations and white certificate schemes: Comparative analysis of experiences in the European Union," Energy Policy, Elsevier, vol. 38(3), pages 1455-1469, March.
    11. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9), pages 1082-1095.
    12. McKenna, Eoghan & Richardson, Ian & Thomson, Murray, 2012. "Smart meter data: Balancing consumer privacy concerns with legitimate applications," Energy Policy, Elsevier, vol. 41(C), pages 807-814.
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