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Predicting winners and losers under time-of-use tariffs using smart meter data

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  • Kiguchi, Y.
  • Weeks, M.
  • Arakawa, R.

Abstract

Time-of-use electricity tariffs may become more widespread as smart meters are installed across deregulated domestic electricity markets. Time-of-use tariffs and other methods of time-dependant pricing can be mutually beneficial, realising a cost reduction for both energy companies and customers if the customer responds to the price signalling. However, such tariffs are likely to create positive and negative financial outcomes for individuals because of customer engagement and potential peak shifting capacity. Identifying potential reducers or non-reducers beforehand can optimise a time-of-use programme design, in turn maximising the outcome of the programme. This paper provides a statistical model to identify the characteristics of so-called winners and losers - or households that would be better or worse off under a time-of-use tariff - using only ex ante information. The model's accuracy reaches a reliable level using historical electricity load and basic household characteristics. This accuracy can be further improved if online activity data is available - providing justification for digital interaction and gamification in time-of-use programmes. This paper also publishes a new public dataset of 1423 households in Japan, including historical smart meter data, household characteristics and online activity variables during the time-of-use intervention period in 2017 and 2018.

Suggested Citation

  • Kiguchi, Y. & Weeks, M. & Arakawa, R., 2021. "Predicting winners and losers under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221016868
    DOI: 10.1016/j.energy.2021.121438
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    as
    1. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    2. Faruqui, Ahmad & Hledik, Ryan & Newell, Sam & Pfeifenberger, Hannes, 2007. "The Power of 5 Percent," The Electricity Journal, Elsevier, vol. 20(8), pages 68-77, October.
    3. Mehrjerdi, Hasan & Hemmati, Reza, 2020. "Energy and uncertainty management through domestic demand response in the residential building," Energy, Elsevier, vol. 192(C).
    4. Ioannis Lampropoulos & Tarek Alskaif & Machteld den Broek & Wilfried Sark & Herre Oostendorp, 2019. "A Method for Developing a Game-Enhanced Tool Targeting Consumer Engagement in Demand Response Mechanisms," Progress in IS, in: Anastasia Stratigea & Dimitris Kavroudakis (ed.), Mediterranean Cities and Island Communities, chapter 0, pages 213-235, Springer.
    5. O'Neill, E. & Weeks, M., 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Cambridge Working Papers in Economics 1865, Faculty of Economics, University of Cambridge.
    6. Lee V. White & Nicole D. Sintov, 2020. "Varied health and financial impacts of time-of-use energy rates across sociodemographic groups raise equity concerns," Nature Energy, Nature, vol. 5(1), pages 16-17, January.
    7. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
    8. Mustafa Alparslan Zehir & Kadir Baris Ortac & Hakan Gul & Alp Batman & Zafer Aydin & João Carlos Portela & Filipe Joel Soares & Mustafa Bagriyanik & Unal Kucuk & Aydogan Ozdemir, 2019. "Development and Field Demonstration of a Gamified Residential Demand Management Platform Compatible with Smart Meters and Building Automation Systems," Energies, MDPI, vol. 12(5), pages 1-18, March.
    9. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    10. Mathiesen, B.V. & Lund, H. & Connolly, D. & Wenzel, H. & Østergaard, P.A. & Möller, B. & Nielsen, S. & Ridjan, I. & Karnøe, P. & Sperling, K. & Hvelplund, F.K., 2015. "Smart Energy Systems for coherent 100% renewable energy and transport solutions," Applied Energy, Elsevier, vol. 145(C), pages 139-154.
    11. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    12. Wang, Yongli & Li, Fang & Yang, Jiale & Zhou, Minhan & Song, Fuhao & Zhang, Danyang & Xue, Lu & Zhu, Jinrong, 2020. "Demand response evaluation of RIES based on improved matter-element extension model," Energy, Elsevier, vol. 212(C).
    13. Antonio Paone & Jean-Philippe Bacher, 2018. "The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art," Energies, MDPI, vol. 11(4), pages 1-19, April.
    14. Ashok, S., 2006. "Peak-load management in steel plants," Applied Energy, Elsevier, vol. 83(5), pages 413-424, May.
    15. Torriti, Jacopo, 2012. "Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy," Energy, Elsevier, vol. 44(1), pages 576-583.
    16. Yilmaz, Selin & Xu, Xiaojing & Cabrera, Daniel & Chanez, Cédric & Cuony, Peter & Patel, Martin K., 2020. "Analysis of demand-side response preferences regarding electricity tariffs and direct load control: Key findings from a Swiss survey," Energy, Elsevier, vol. 212(C).
    17. Campillo, Javier & Dahlquist, Erik & Wallin, Fredrik & Vassileva, Iana, 2016. "Is real-time electricity pricing suitable for residential users without demand-side management?," Energy, Elsevier, vol. 109(C), pages 310-325.
    18. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    19. Johnson, Daniel & Horton, Ella & Mulcahy, Rory & Foth, Marcus, 2017. "Gamification and serious games within the domain of domestic energy consumption: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 249-264.
    20. Peter C. Reiss & Matthew W. White, 2005. "Household Electricity Demand, Revisited," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 853-883.
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    4. Choi, Dong Gu & Murali, Karthik, 2022. "The impact of heterogeneity in consumer characteristics on the design of optimal time-of-use tariffs," Energy, Elsevier, vol. 254(PB).
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    6. Yousaf Murtaza Rind & Muhammad Haseeb Raza & Muhammad Zubair & Muhammad Qasim Mehmood & Yehia Massoud, 2023. "Smart Energy Meters for Smart Grids, an Internet of Things Perspective," Energies, MDPI, vol. 16(4), pages 1-35, February.
    7. Huang, He & Wang, Honglei & Hu, Yu-Jie & Li, Chengjiang & Wang, Xiaolin, 2022. "Optimal plan for energy conservation and CO2 emissions reduction of public buildings considering users' behavior: Case of China," Energy, Elsevier, vol. 261(PA).

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