{"title":"基于GA-PSO混合规划算法的电力客户信用评价模型","authors":"W. Xinli","doi":"10.1109/ICSSEM.2012.6340713","DOIUrl":null,"url":null,"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.","PeriodicalId":115037,"journal":{"name":"2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization","volume":"189 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"The credit evaluation model of electricity customer based on GA-PSO hybrid programming algorithm\",\"authors\":\"W. Xinli\",\"doi\":\"10.1109/ICSSEM.2012.6340713\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":115037,\"journal\":{\"name\":\"2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization\",\"volume\":\"189 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSSEM.2012.6340713\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSSEM.2012.6340713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The credit evaluation model of electricity customer based on GA-PSO hybrid programming algorithm
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.