IDEAS home Printed from https://ideas.repec.org/a/igg/jiit00/v21y2025i1p1-23.html
   My bibliography  Save this article

An Improved Density Peak Clustering Method for Power Load Anomaly Detection

Author

Listed:
  • Han Jianfeng

    (Tianjin University of Commerce, China)

  • Chen Xinxin

    (Tianjin University of Commerce, China)

  • Wang Li

    (Hebei University of Technology, China)

  • Fan Shurui

    (Hebei University of Technology, China)

  • Zhang Yong

    (Tianjin University of Commerce, China)

Abstract

Aimed at the sensitivity of the cut-off distance parameter, the difficulty in accurately determining cluster centers, and the criteria unreliable for anomalous data selection, an improved density peak clustering combined with local outlier factor (LOF) algorithm is proposed for power load anomaly detection. Firstly, the density entropy algorithm is constructed to achieve the adaptive selection of cut-off distance, and the statistical quartile method is used for threshold setting to achieve the adaptive cluster center selection. Secondly, the LOF algorithm was developed to increase the rate of anomaly detection and address the absence of criteria for selecting anomalous data. Finally, the proposed algorithm is experimentally validated on a real power dataset, and the results demonstrate that the improved algorithm could be able to effectively detect the power loads anomaly data.

Suggested Citation

  • Han Jianfeng & Chen Xinxin & Wang Li & Fan Shurui & Zhang Yong, 2025. "An Improved Density Peak Clustering Method for Power Load Anomaly Detection," International Journal of Intelligent Information Technologies (IJIIT), IGI Global Scientific Publishing, vol. 21(1), pages 1-23, January.
    • Handle: RePEc:igg:jiit00:v:21:y:2025:i:1:p:1-23
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.385734
    Download Restriction: no
    ---><---

    More about this item

    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:igg:jiit00:v:21:y:2025:i:1:p:1-23. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.