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Energy Hogs and Misers: Magnitude and Variability of Individuals’ Household Electricity Consumption

Author

Listed:
  • Claudia Bustamante

    (Institute for a Sustainable Environment, Clarkson University, Potsdam, NY 13699, USA)

  • Stephen Bird

    (Institute for a Sustainable Environment and Political Science, Clarkson University, Potsdam, NY 13699, USA)

  • Lisa Legault

    (Department of Psychology, Clarkson University, Potsdam, NY 13699, USA)

  • Susan E. Powers

    (Institute for a Sustainable Environment, Clarkson University, Potsdam, NY 13699, USA)

Abstract

We use circuit-level granular electricity measurements from student housing and statistical analysis to better understand individuals’ electricity consumption. Two key patterns emerged—individuals varied systematically in their magnitude of electricity use as well as in their variability of usage at the hourly and daily level. A cluster analysis of electricity consumption in individual bedrooms shows that 18% of students consume 48% of total electricity use at a median of 2.17 kWh/d/person. These few energy hogs have a disproportionate impact on electricity consumption. In contrast, the misers (22% of students) consume only 4% of the electricity (0.18 kWh/d/person). Mini-refrigerators in bedrooms contributed substantially to the total electricity use of the moderate users. In contrast, mini-refrigerators were less influential for energy hogs, suggesting that these residents may draw power in others ways, such as by using powerful computing or gaming systems for hours each day. A sub-cluster analysis revealed substantial individual variability in hourly usage profiles. Some energy hogs use electricity consistently throughout the day, while others have specific periods of high consumption. We demonstrate how our analysis is generalizable to other situations where the resident does not directly pay their utility bills and thus has limited financial incentive to conserve, and how it contributes to a deeper understanding of the different ways in which individuals use energy. This allows for targeting interventions to groups with similar patterns of consumption. For example, policies such as fines or fees that might reduce the excessive electricity use for short times or for individual hogs could result in potential savings ranging from 16–33% of bedroom electricity.

Suggested Citation

  • Claudia Bustamante & Stephen Bird & Lisa Legault & Susan E. Powers, 2023. "Energy Hogs and Misers: Magnitude and Variability of Individuals’ Household Electricity Consumption," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4171-:d:1080296
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    References listed on IDEAS

    as
    1. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    2. Bird, Stephen & Hernández, Diana, 2012. "Policy options for the split incentive: Increasing energy efficiency for low-income renters," Energy Policy, Elsevier, vol. 48(C), pages 506-514.
    3. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    4. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    5. Murtagh, Niamh & Nati, Michele & Headley, William R. & Gatersleben, Birgitta & Gluhak, Alexander & Imran, Muhammad Ali & Uzzell, David, 2013. "Individual energy use and feedback in an office setting: A field trial," Energy Policy, Elsevier, vol. 62(C), pages 717-728.
    6. Delmas, Magali A. & Fischlein, Miriam & Asensio, Omar I., 2013. "Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012," Energy Policy, Elsevier, vol. 61(C), pages 729-739.
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