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A novel machine learning approach for identifying the drivers of domestic electricity users' price responsiveness

Author

Listed:
  • Peiyang Guo

    (Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong)

  • Jacqueline CK Lam

    (Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong)

  • Victor OK Li

    (Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong)

Abstract

No abstract is available for this item.

Suggested Citation

  • Peiyang Guo & Jacqueline CK Lam & Victor OK Li, 2018. "A novel machine learning approach for identifying the drivers of domestic electricity users' price responsiveness," Working Papers EPRG 1824, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg1824
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    File URL: https://www.jbs.cam.ac.uk/wp-content/uploads/2023/12/eprg-wp1824.pdf
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    More about this item

    Keywords

    Time-based electricity pricing; price responsiveness; high-potential users; variable selection; Time of Use; machine learning;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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