• DocumentCode
    3365193
  • Title

    Research on the Electricity Customer Credit Evaluation Based on Fuzzy Expected Value Decision-making Method Modified by Least Squares Support Vector Machine

  • Author

    Mian Xing

  • Author_Institution
    Sch. of Math. & Phys., North China Electr. Power Univ., Beijing
  • fYear
    2008
  • fDate
    4-6 Nov. 2008
  • Firstpage
    367
  • Lastpage
    372
  • Abstract
    In view of electricity customer credit evaluation lacking of precise index system and hardly quantifying subjective factors and experience factors, fuzzy expected value decision-making method modified by least squares support vector machine (LS-SVM) is presented. Firstly, electricity customer credit evaluation index system is constructed; the indices values and subjective experiences values are given in the form of triangular fuzzy numbers. Then credit expected values are resulted by fuzzy expected value decision-making method. Finally, LS-SVM based on the principle of structural risk minimization modifies the expected values. The experiment shows that the credit grades after the modification suit to the original credit grades enacted by power supply enterprises and are more practicable.
  • Keywords
    customer services; decision making; electricity supply industry; fuzzy set theory; least squares approximations; power engineering computing; risk management; support vector machines; LS-SVM; electricity customer credit evaluation; fuzzy expected value decision-making method; least squares support vector machine; power supply enterprises; structural risk minimization; triangular fuzzy numbers; Decision making; Fuzzy systems; Least squares methods; Mathematics; Physics; Power supplies; Power system modeling; Research and development management; Risk management; Support vector machines; Fuzzy; Support Vector; evaluation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Risk Management & Engineering Management, 2008. ICRMEM '08. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-0-7695-3402-2
  • Type

    conf

  • DOI
    10.1109/ICRMEM.2008.109
  • Filename
    4673257