• DocumentCode
    575778
  • Title

    The credit evaluation model of electricity customer based on GA-PSO hybrid programming algorithm

  • Author

    Xinli, Wang

  • Author_Institution
    Econ. & Manage. Dept., North China Electr. Power Univ., Baoding, China
  • Volume
    1
  • fYear
    2012
  • fDate
    20-21 Oct. 2012
  • Firstpage
    222
  • Lastpage
    225
  • Abstract
    Power supply enterprises face the business risk caused by electricity clients who break their promise on supply contracts. In order to avoid credit risk and conduct comprehensive evaluation on electricity clients, this paper builds an electricity client credit risk evaluation model based on GPSO hybrid algorithm, overcoming the shortcomings of traditional linear ECCR evaluation method. This new model integrates advantages of GA (genetic algorithm) and PSO, better than traditional multiple regression method and GP method regarding convergence performance and forecast accuracy. Simulation results indicate that hybrid model is simple and feasible, and it can improve efficiency and accuracy of evaluation.
  • Keywords
    convergence; credit transactions; genetic algorithms; particle swarm optimisation; power markets; regression analysis; risk analysis; GA PSO; business risk; convergence performance; electricity client credit risk evaluation model; electricity customer; genetic algorithm; hybrid programming algorithm; linear ECCR evaluation method; particle swarm optimisation; power supply enterprise; regression method; supply contract; Accuracy; Data models; Electricity; Genetic algorithms; Optimization; Predictive models; Programming; electricity client credit evaluation; genetic algorithm; hybrid particle swarm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2012 3rd International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4673-0914-1
  • Type

    conf

  • DOI
    10.1109/ICSSEM.2012.6340713
  • Filename
    6340713