The Model Of Credit Risk Assessment In Power Industry Base On RS-SVM

With the gradual deepening of power system, power supply and demand situation in China has greatly changed, the credit of electricity power industry enterprises is a important issues which they are facing, not only affect the business operations and corporate image, but also related to survival and development of enterprises, so it is necessary to evaluate the credit of the power industry, thereby circumventing the problem of power companies for credit risks. In terms of customer demand for electricity, it is very important to accurately determine the level of power companies'credit.This article based on the current status of the power industry's credit assessment, established credit evaluation index system of power industry. For the features of a large number of indicators, proposed based on rough sets and support vector machine credit evaluation model. Using rough sets of the attribute reduction algorithm to realize evaluation index reduction, Using support vector machine (SVM) is constructed, the regression algorithm has better generalization ability and the suppressing noise regression model, with real data index selection, weight calculation and comprehensive assessment, determine the electric power enterprise credit evaluation of comprehensive value, obtains each electric power enterprises optimized credit evaluation order rankings. And with the single support vector machine (SVM) model compared to verify the selected the effectiveness of the method, the results of the study show that the model is feasible, and good effects. Finally, based on the current situation of the electric power enterprise credit evaluation actuality and existing problems, and put forward some proposals.