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Household power usage pattern filtering-based residential electricity plan recommender system

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  • Zhao, Pengxiang
  • Dong, Zhao Yang
  • Meng, Ke
  • Kong, Weicong
  • Yang, Jiajia

Abstract

Deregulation of the retail electricity market has led to the emergence of an increasing number of electricity plans with competitive rates. Electricity customers now have more flexibility in choosing an electricity provider and electricity plan based on individual consumption needs. In this paper, a feature engineering hybrid collaborative filtering-based electricity plan recommender system (FECF-EPRS) is proposed for helping the customer get the right electricity plan. This system is composed of three-segment models for missing feature estimation, feature crosses construction, and electricity plan recommendation. It only takes easy-to-obtain household appliance usage features as inputs and outputs ratings for different plans. Through the test of real electricity market data, the FECF-EPRS shows a greater improvement in terms of recommendation accuracy, which can provide more accurate recommendations to customers and more reasonable pricing references for retailers.

Suggested Citation

  • Zhao, Pengxiang & Dong, Zhao Yang & Meng, Ke & Kong, Weicong & Yang, Jiajia, 2021. "Household power usage pattern filtering-based residential electricity plan recommender system," Applied Energy, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921006176
    DOI: 10.1016/j.apenergy.2021.117191
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    References listed on IDEAS

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    1. Gupta, Ankit & Mehrotra, Kishan G. & Mohan, Chilukuri, 2010. "A clustering-based discretization for supervised learning," Statistics & Probability Letters, Elsevier, vol. 80(9-10), pages 816-824, May.
    2. Tsai, Men-Shen & Lin, Yu-Hsiu, 2012. "Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation," Applied Energy, Elsevier, vol. 96(C), pages 55-73.
    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. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.
    5. Pamulapati, Trinadh & Mallipeddi, Rammohan & Lee, Minho, 2020. "Multi-objective home appliance scheduling with implicit and interactive user satisfaction modelling," Applied Energy, Elsevier, vol. 267(C).
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    Cited by:

    1. vom Scheidt, Frederik & Staudt, Philipp, 2024. "A data-driven Recommendation Tool for Sustainable Utility Service Bundles," Applied Energy, Elsevier, vol. 353(PB).
    2. Qiu, Dawei & Wang, Yi & Wang, Junkai & Jiang, Chuanwen & Strbac, Goran, 2023. "Personalized retail pricing design for smart metering consumers in electricity market," Applied Energy, Elsevier, vol. 348(C).
    3. Agyeman, Stephen Duah & Lin, Boqiang, 2023. "Electricity industry (de)regulation and innovation in negative-emission technologies: How do market liberalization influences climate change mitigation?," Energy, Elsevier, vol. 270(C).

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