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Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach

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  • Guo, Peiyang
  • Lam, Jacqueline C.K.
  • Li, Victor O.K.

Abstract

Time-based pricing for domestic electricity users has been effective in reducing peak demand and facilitating integration of renewable energies. However, high cost, price non-responsiveness and adverse selection present challenges. To tackle these challenges, it would be important to investigate which users exhibit a higher potential to respond to price change, such that time-based pricing can be introduced to such users. Few studies have examined which users are more price-responsive and what drives price responsiveness. This article aims to fill this gap by comprehensively identifying the drivers that determine electricity users’ price responsiveness, in order to facilitate the selection of high potential users. We adopt a novel machine-learning approach to select the high potential users, using the Irish smart meter dataset (2009–10), which forms part of the national Time of Use trial. Our methodological novelties cover two aspects: First, using a feed-forward neural network model, we aim to determine precisely the price responsiveness of individual households, and address the nonlinearity of energy consumption and price response attributes. Second, we apply an integrated machine learning methodology to identify the drivers of price responsiveness. Our integrated approach outperforms the traditional variable selection methods by identifying drivers that are reliable. Our empirical results have shown that demographic and residential characteristics, psychological factors, historical electricity consumptionand appliance ownership are significant drivers. In particular, historical electricity consumption, income, household size, perceived behavioural control, and adoption of specific appliances, including immersion water heater and dishwasher, are significant drivers of price responsiveness across the Irish electricity users. Our results have also verified that continual price increase within a moderate range will not drive additional peak demand reduction; there is an intention-behaviour gap, where stated intention does not lead to actual peak reduction behaviours. Based on such results, we have conducted a scenario analysis to demonstrate the feasibility of selecting high potential users of achieving significant peak electricity reduction.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:235:y:2019:i:c:p:900-913
    DOI: 10.1016/j.apenergy.2018.11.014
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    2. Chen, Peipei & Wu, Yi & Zhong, Honglin & Long, Yin & Meng, Jing, 2022. "Exploring household emission patterns and driving factors in Japan using machine learning methods," Applied Energy, Elsevier, vol. 307(C).
    3. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
    4. 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).
    5. Gržanić, M. & Capuder, T. & Zhang, N. & Huang, W., 2022. "Prosumers as active market participants: A systematic review of evolution of opportunities, models and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    6. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).

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