IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-256-9_127.html

Research on Power Marketing Decision-Making Algorithm Based on Bayesian Network

In: Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)

Author

Listed:
  • GUO Ying

    (Marketing Service Center (Metering Center) of State Grid Hubei Electric Power Co., Ltd)

  • NI Yang

    (Marketing Service Center (Metering Center) of State Grid Hubei Electric Power Co., Ltd)

  • WANG Yuan

    (Marketing Service Center (Metering Center) of State Grid Hubei Electric Power Co., Ltd)

  • ZHAO Jing

    (Marketing Service Center (Metering Center) of State Grid Hubei Electric Power Co., Ltd)

  • LIU Liwei

    (Marketing Service Center (Metering Center) of State Grid Hubei Electric Power Co., Ltd)

Abstract

With the rapid development of the economy, fully leveraging the priority role of electricity is of great significance for accelerating the modernization of electricity, continuously meeting the growing demand for electricity and social production and consumption, and promoting socio-economic development. Among them, the role of electricity marketing is becoming increasingly prominent. How to fully mine and utilize the large amount of power marketing data accumulated by electric power enterprises over the years, so as to provide reliable support for the analysis and research of power marketing decisions. Bayesian network is a graphical pattern used to represent the continuous probability distribution of a set of variables, which provides causal information to discover potential relationships between data, and because of these characteristics, it is widely used in data mining. Therefore, this paper applies Bayesian network to the analysis and research of power marketing decision, and establishes a Bayesian network suitable for power marketing decision-making for customer value evaluation and provides reliable support for marketing decision-making.

Suggested Citation

  • GUO Ying & NI Yang & WANG Yuan & ZHAO Jing & LIU Liwei, 2024. "Research on Power Marketing Decision-Making Algorithm Based on Bayesian Network," Advances in Economics, Business and Management Research, in: Suhaiza Hanim Binti Dato Mohamad Zailani & Kosga Yagapparaj & Norhayati Zakuan (ed.), Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), pages 1245-1253, Springer.
  • Handle: RePEc:spr:advbcp:978-94-6463-256-9_127
    DOI: 10.2991/978-94-6463-256-9_127
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:advbcp:978-94-6463-256-9_127. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.