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
Link To Document