IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v78y2014icp397-410.html
   My bibliography  Save this article

Revealing household characteristics from smart meter data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544214011748
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2014.10.025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9-10), pages 1082-1095, October.
    4. 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.
    5. 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.
    6. 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.
    7. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9), pages 1082-1095.
    8. 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.
    9. 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.
    10. 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.
    11. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kwonsik Song & Kyle Anderson & SangHyun Lee & Kaitlin T. Raimi & P. Sol Hart, 2020. "Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data," Energies, MDPI, vol. 13(14), pages 1-21, July.
    2. Akito Ozawa & Ryota Furusato & Yoshikuni Yoshida, 2017. "Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan," Sustainability, MDPI, vol. 9(4), pages 1-23, March.
    3. Schleich, Joachim & Gassmann, Xavier & Faure, Corinne & Meissner, Thomas, 2016. "Making the implicit explicit: A look inside the implicit discount rate," Energy Policy, Elsevier, vol. 97(C), pages 321-331.
    4. Bonan, Jacopo & Battiston, Pietro & Bleck, Jaimie & LeMay-Boucher, Philippe & Pareglio, Stefano & Sarr, Bassirou & Tavoni, Massimo, 2021. "Social interaction and technology adoption: Experimental evidence from improved cookstoves in Mali," World Development, Elsevier, vol. 144(C).
    5. Choi, Sunghee & Hwang, Seok-Joon & Denzau, Arthur T., 2021. "Do households conserve electricity when they receive signals of greater consumption than neighbours? The Korean case," Energy, Elsevier, vol. 225(C).
    6. Egebark, Johan & Ekström, Mathias, 2016. "Can indifference make the world greener?," Journal of Environmental Economics and Management, Elsevier, vol. 76(C), pages 1-13.
    7. Hobman, Elizabeth V. & Frederiks, Elisha R. & Stenner, Karen & Meikle, Sarah, 2016. "Uptake and usage of cost-reflective electricity pricing: Insights from psychology and behavioural economics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 455-467.
    8. Song, Kwonsik & Anderson, Kyle & Lee, SangHyun, 2020. "An energy-cyber-physical system for personalized normative messaging interventions: Identification and classification of behavioral reference groups," Applied Energy, Elsevier, vol. 260(C).
    9. Konstantin Hopf & Mariya Sodenkamp & Thorsten Staake, 2018. "Enhancing energy efficiency in the residential sector with smart meter data analytics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 453-473, November.
    10. Djula Borozan & Mirjana Radman Funaric, 2018. "The Impact of Disaggregated Social Capital on Household Electricity Intensity," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 16(2), pages 189-207.
    11. Tiefenbeck, Verena & Staake, Thorsten & Roth, Kurt & Sachs, Olga, 2013. "For better or for worse? Empirical evidence of moral licensing in a behavioral energy conservation campaign," Energy Policy, Elsevier, vol. 57(C), pages 160-171.
    12. Lucas, Sterenn & Salladarré, Frédéric & Brécard, Dorothée, 2018. "Green consumption and peer effects: Does it work for seafood products?," Food Policy, Elsevier, vol. 76(C), pages 44-55.
    13. Rita Abdel Sater, 2021. "Essays on the application of behavioural insights to environmental policy [Essais sur l’application des connaissances comportementales aux politiques environnementales]," SciencePo Working papers tel-03450909, HAL.
    14. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.
    15. Elisha R. Frederiks & Karen Stenner & Elizabeth V. Hobman, 2015. "The Socio-Demographic and Psychological Predictors of Residential Energy Consumption: A Comprehensive Review," Energies, MDPI, vol. 8(1), pages 1-37, January.
    16. Shimoda, Yoshiyuki & Yamaguchi, Yohei & Iwafune, Yumiko & Hidaka, Kazuyoshi & Meier, Alan & Yagita, Yoshie & Kawamoto, Hisaki & Nishikiori, Soichi, 2020. "Energy demand science for a decarbonized society in the context of the residential sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    17. Todd D. Gerarden & Richard G. Newell & Robert N. Stavins, 2017. "Assessing the Energy-Efficiency Gap," Journal of Economic Literature, American Economic Association, vol. 55(4), pages 1486-1525, December.
    18. Fischbacher, Urs & Schudy, Simeon & Teyssier, Sabrina, 2021. "Heterogeneous preferences and investments in energy saving measures," Resource and Energy Economics, Elsevier, vol. 63(C).
    19. Ajla Cosic & Hana Cosic & Sebastian Ille, 2018. "Can nudges affect students' green behaviour? A field experiment," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 2(1), pages 107-111, March.
    20. Bartels, Lara & Kesternich, Martin, 2022. "Motivate the crowd or crowd- them out? The impact of local government spending on the voluntary provision of a green public good," ZEW Discussion Papers 22-040, ZEW - Leibniz Centre for European Economic Research.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:78:y:2014:i:c:p:397-410. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.