IDEAS home Printed from https://ideas.repec.org/a/eee/juipol/v90y2024ics0957178724001000.html
   My bibliography  Save this article

A fair grid connection cost-sharing model for electricity based on the random forest machine learning method

Author

Listed:
  • Xie, Li
  • Kong, Chun

Abstract

Developing countries should clarify the dedicated grid connection cost-sharing model when reforming electricity user price policy, which could be learned from developed countries’ experiences. We use the random forest method and monthly data from several developed countries or regions, including the United States and the United Kingdom in 2018–2020, to identify key features that influence decision-makers in choosing dedicated grid connection cost-sharing models and simulate the connection cost-sharing models suitable for various regions of China using provincial data. It provides experience and enlightenment for China and other developing countries to implement the connection price policy reform.

Suggested Citation

  • Xie, Li & Kong, Chun, 2024. "A fair grid connection cost-sharing model for electricity based on the random forest machine learning method," Utilities Policy, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:juipol:v:90:y:2024:i:c:s0957178724001000
    DOI: 10.1016/j.jup.2024.101807
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0957178724001000
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jup.2024.101807?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:juipol:v:90:y:2024:i:c:s0957178724001000. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.sciencedirect.com/journal/utilities-policy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.