• DocumentCode
    2124837
  • Title

    Application of Data Mining in Arrear Risks Prediction of Power Customer

  • Author

    Wang, Jing-min ; Wen, Yu-qian

  • Author_Institution
    North China Electr. Power Univ., Baoding
  • fYear
    2008
  • fDate
    21-22 Dec. 2008
  • Firstpage
    206
  • Lastpage
    210
  • Abstract
    Difficulties in collection of electric toll have affected the normal operation and development of power supply bureau seriously. So the arrear problem of power customer has become one of the focus questions that power supply bureau pays attention to. In this paper, based on information entropy theory and on data mining technology, we have proposed a new data mining approach. This approach can measure the information amount of each index which was used to predict the arrear risks of power customer. With this approach, we have got an index system that can reflect the arrear risks. And then, support vector machines (SVM) was used to predict the arrear risks. Through empirical analysis, it has shown that the proposed approach used to arrear risk prediction had higher classification accuracy. So, it is promising to arrear risks prediction of power customer.
  • Keywords
    consumer behaviour; data mining; power engineering computing; power system management; risk management; SVM; data mining; information entropy theory; power customer; power supply bureau development; support vector machines; Costs; Data mining; Energy consumption; Information entropy; Power supplies; Power system modeling; Predictive models; Risk analysis; Support vector machine classification; Support vector machines; SVM; arrear risks; data mining; information entropy; power customer;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge Acquisition and Modeling, 2008. KAM '08. International Symposium on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3488-6
  • Type

    conf

  • DOI
    10.1109/KAM.2008.134
  • Filename
    4732816