IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i3p716-d1583458.html
   My bibliography  Save this article

Accident Factors Importance Ranking for Intelligent Energy Systems Based on a Novel Data Mining Strategy

Author

Listed:
  • Rongbin Li

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Jian Zhang

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Fangming Deng

    (School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China)

Abstract

As global energy networks expand and smart grid technology evolves rapidly, the volume of historical power accident data has increased dramatically, containing valuable risk information that is essential for building efficient public safety early warning systems. This paper introduces an innovative text analysis method, the Sparse Coefficient Optimized Weighted FP-Growth Algorithm (SCO-WFP), which is designed to optimize the processing of power accident-related textual data and more effectively uncover hidden patterns behind accidents. The method enhances the evaluation of sparse risk factors by preprocessing, clustering analysis, and calculating piecewise weights of power accident data. The SCO-WFP algorithm is then applied to extract frequent itemsets, revealing deep associations between accident severity and risk factors. Experimental results show that, compared to traditional methods, the SCO-WFP algorithm significantly improves both accuracy and execution speed. The findings demonstrate the method’s effectiveness in mining frequent itemsets from text semantics, facilitating a deeper understanding of the relationship between risk factors and accident severity.

Suggested Citation

  • Rongbin Li & Jian Zhang & Fangming Deng, 2025. "Accident Factors Importance Ranking for Intelligent Energy Systems Based on a Novel Data Mining Strategy," Energies, MDPI, vol. 18(3), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:716-:d:1583458
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/3/716/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/3/716/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xinghua Wang & Xixian Liu & Fucheng Zhong & Zilv Li & Kaiguo Xuan & Zhuoli Zhao, 2023. "A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    2. Jiufu Liu & Hongzhong Ma & Xiaolei Xie & Jun Cheng, 2022. "Short Text Classification for Faults Information of Secondary Equipment Based on Convolutional Neural Networks," Energies, MDPI, vol. 15(7), pages 1-15, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leijiao Ge & Jun Yan & Yonghui Sun & Zhongguan Wang, 2022. "Situational Awareness for Smart Distribution Systems," Energies, MDPI, vol. 15(11), pages 1-3, June.

    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:gam:jeners:v:18:y:2025:i:3:p:716-:d:1583458. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.