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Research and application progress of data mining technology in electric power system

Fangwei NING Yan SHI Yishu CAI Weiqing XU

Fangwei NING, Yan SHI, Yishu CAI, Weiqing XU. Research and application progress of data mining technology in electric power system[J]. 先进制造科学与技术, 2021, 1(3): 2021007. doi: 10.51393/j.jamst.2021007
引用本文: Fangwei NING, Yan SHI, Yishu CAI, Weiqing XU. Research and application progress of data mining technology in electric power system[J]. 先进制造科学与技术, 2021, 1(3): 2021007. doi: 10.51393/j.jamst.2021007
Fangwei NING, Yan SHI, Yishu CAI, Weiqing XU. Research and application progress of data mining technology in electric power system[J]. Journal of Advanced Manufacturing Science and Technology , 2021, 1(3): 2021007. doi: 10.51393/j.jamst.2021007
Citation: Fangwei NING, Yan SHI, Yishu CAI, Weiqing XU. Research and application progress of data mining technology in electric power system[J]. Journal of Advanced Manufacturing Science and Technology , 2021, 1(3): 2021007. doi: 10.51393/j.jamst.2021007

Research and application progress of data mining technology in electric power system

doi: 10.51393/j.jamst.2021007
详细信息
    通讯作者:

    Yan SHI,E-mail:nfangwei@163.com

Research and application progress of data mining technology in electric power system

  • 摘要:

    With the rapid development of computer technology and the improvement of intelligent technologies in electric power engineering, the volume of data has increased exponentially. Data mining technology can be utilized to search information hidden in the huge amounts of data, and then the data can be transformed into useful knowledge to promote the development of electric power technology. In order to be acquainted with the research and application progress of data mining technology in electric power engineering, several major data mining algorithms are introduced in this paper, including ANN (Artificial Neural Network) algorithm, SVM (Support Vector Machine) algorithm, decision tree algorithm, K-means algorithm, NBC (Naive Bayesian Classification) algorithm and Apriori algorithm. And then, the methods of data mining technology in prediction, classification, clustering and association rules analysis are explained in detail in this engineering, which are combined with the electricity price prediction, power load forecasting, fault type identification, system state classification, power generation side association rules, power grid operation data association analysis. At last, this technology in electric power engineering is summarized and an expectation for the future development is provided.

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出版历程
  • 收稿日期:  2021-04-25
  • 修回日期:  2021-05-10
  • 网络出版日期:  2021-06-22
  • 刊出日期:  2021-05-19

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