• DocumentCode
    2228978
  • Title

    Research of evaluating credit-risk in power enterprise based on SVM and VIKOR method

  • Author

    Huang, Yuansheng ; Yan, Ying

  • Author_Institution
    Dept. of Economic & Manage., North China Electr. Power Univ., Baoding, China
  • fYear
    2008
  • fDate
    8-11 Dec. 2008
  • Firstpage
    1596
  • Lastpage
    1599
  • Abstract
    Some clients are in arrears with a great amount of electricity charges in operation of power system, which has seriously hindered the healthy development of electric industry. Evaluation of clients¿ credit is an important problem of management in power supply enterprises.Considering the training time and the accuracy, a new algorithm based on Support Vector Machine (SVM) and VIKOR method is adopted to solve this problem. Support Vector Machine (SVM) was proposed to solve the small sample learning problem. VIKOR method was developed to solve decision problems with conflicting and with different units criteria.Finally, an application example has been given to test the feasibility and effectiveness of the proposed method.
  • Keywords
    electricity supply industry; support vector machines; SVM; VIKOR method; credit-risk evaluation; electric industry; electricity charges; power supply enterprises; Electricity supply industry; Energy management; Industrial training; Load management; Machine learning; Management training; Power supplies; Power system management; Power systems; Support vector machines; SVM; VIKOR method; classification; credit-risk; risk management;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Engineering and Engineering Management, 2008. IEEM 2008. IEEE International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4244-2629-4
  • Electronic_ISBN
    978-1-4244-2630-0
  • Type

    conf

  • DOI
    10.1109/IEEM.2008.4738141
  • Filename
    4738141