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Reducing household electricity demand through smart metering: The role of improved information about energy saving

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  • Carroll, James
  • Lyons, Seán
  • Denny, Eleanor

Abstract

The international roll out of residential smart meters has increased considerably in recent years. The improved consumption feedback provided, and in particular, the installation of in-house displays, has been shown to significantly reduce residential electricity demand in some international trials. This paper attempts to uncover the underlying drivers of such information-led reductions by exploring two research questions. First, does feedback improve a household's stock of information about potential energy reducing behaviours? And second, do improvements in such information help explain the demand reductions associated with the introduction of smart metering and time-of-use tariffs? Data is from a randomised controlled smart metering trial (Ireland) which also collected extensive information on household attitudes towards energy conservation and self-reported stocks of information related to energy saving. As with previous results in Ireland, we find that participation in a smart metering programme with time-of-use tariffs significantly reduces demand. Although treated households also increased their self-reported energy-reducing information, such improvements are not correlated with demand reductions in the short-run. Given this result, it is possible that feedback and other information provided in the context of smart metering are mainly effective in reducing and shifting demand because they act as a reminder and motivator.

Suggested Citation

  • Carroll, James & Lyons, Seán & Denny, Eleanor, 2014. "Reducing household electricity demand through smart metering: The role of improved information about energy saving," Energy Economics, Elsevier, vol. 45(C), pages 234-243.
  • Handle: RePEc:eee:eneeco:v:45:y:2014:i:c:p:234-243
    DOI: 10.1016/j.eneco.2014.07.007
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    References listed on IDEAS

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    Cited by:

    1. Wesley Angelino de Souza & Fernando Deluno Garcia & Fernando Pinhabel Marafão & Luiz Carlos Pereira da Silva & Marcelo Godoy Simões, 2019. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition," Energies, MDPI, Open Access Journal, vol. 12(14), pages 1-18, July.
    2. Schubert, Christian, 2017. "Green nudges: Do they work? Are they ethical?," Ecological Economics, Elsevier, vol. 132(C), pages 329-342.
    3. Yilmaz, S. & Weber, S. & Patel, M.K., 2019. "Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes," Energy Policy, Elsevier, vol. 133(C).
    4. Dorothee Charlier and Berangere Legendre, 2019. "A Multidimensional Approach to Measuring Fuel Poverty," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    5. Buckley, Penelope, 2020. "Prices, information and nudges for residential electricity conservation: A meta-analysis," Ecological Economics, Elsevier, vol. 172(C).
    6. Shirley Pon, 2017. "The Effect of Information on TOU Electricity Use: an Irish residential study," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    7. Yang, Changhui & Meng, Chen & Zhou, Kaile, 2018. "Residential electricity pricing in China: The context of price-based demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2870-2878.
    8. Regan, Mark & Keane, Claire & Walsh, John R, 2018. "Using behavioural experiments to pre-test policy," Papers BP2019/2, Economic and Social Research Institute (ESRI).
    9. Fujimi, Toshio & Kajitani, Yoshio & Chang, Stephanie E., 2016. "Effective and persistent changes in household energy-saving behaviors: Evidence from post-tsunami Japan," Applied Energy, Elsevier, vol. 167(C), pages 93-106.
    10. Khanna, Nina Zheng & Guo, Jin & Zheng, Xinye, 2016. "Effects of demand side management on Chinese household electricity consumption: Empirical findings from Chinese household survey," Energy Policy, Elsevier, vol. 95(C), pages 113-125.
    11. Huh, Sung-Yoon & Woo, JongRoul & Lim, Sesil & Lee, Yong-Gil & Kim, Chang Seob, 2015. "What do customers want from improved residential electricity services? Evidence from a choice experiment," Energy Policy, Elsevier, vol. 85(C), pages 410-420.
    12. Düştegör, Dilek & Sultana, Nahid & Felemban, Noor & Al Qahtani, Deemah, 2018. "A smarter electricity grid for the Eastern Province of Saudi Arabia: Perceptions and policy implications," Utilities Policy, Elsevier, vol. 50(C), pages 26-39.
    13. Strong, Derek Ryan, 2017. "The Early Diffusion of Smart Meters in the US Electric Power Industry," Thesis Commons 7zprk, Center for Open Science.
    14. Guo, Zhifeng & Zhou, Kaile & Zhang, Chi & Lu, Xinhui & Chen, Wen & Yang, Shanlin, 2018. "Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 399-412.
    15. Jooseok Oh, 2020. "IoT-Based Smart Plug for Residential Energy Conservation: An Empirical Study Based on 15 Months’ Monitoring," Energies, MDPI, Open Access Journal, vol. 13(15), pages 1-13, August.
    16. Wei Song & Yifei Tian & Simon Fong & Kyungeun Cho & Wei Wang & Weiqiang Zhang, 2016. "GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance," Sustainability, MDPI, Open Access Journal, vol. 8(10), pages 1-20, September.
    17. Anderson, Ben & Torriti, Jacopo, 2018. "Explaining shifts in UK electricity demand using time use data from 1974 to 2014," Energy Policy, Elsevier, vol. 123(C), pages 544-557.
    18. Aydin, Erdal & Brounen, Dirk & Kok, Nils, 2018. "Information provision and energy consumption: Evidence from a field experiment," Energy Economics, Elsevier, vol. 71(C), pages 403-410.
    19. Bernadeta Gołębiowska & Anna Bartczak & Wiktor Budziński, 2019. "Impact of social comparison on DSM in Poland," Working Papers 2019-10, Faculty of Economic Sciences, University of Warsaw.
    20. Fei Wang & Liming Liu & Yili Yu & Gang Li & Jessica Li & Miadreza Shafie-khah & João P. S. Catalão, 2018. "Impact Analysis of Customized Feedback Interventions on Residential Electricity Load Consumption Behavior for Demand Response," Energies, MDPI, Open Access Journal, vol. 11(4), pages 1-22, March.

    More about this item

    Keywords

    Residential electricity demand; Smart meters; Consumption feedback; Household knowledge; Conservation motivations;

    JEL classification:

    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation
    • D10 - Microeconomics - - Household Behavior - - - General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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