China's Power Supply SVM Regression Forecast Based on Rough Set Attribute Reduction
Cheng Baibin · Computer Technology and Development · 2010
The application of RSSVR method,which is support vector machine regression(SVR) based on attribute reduction algorithm of rough sets,on forecast of China's power supply is dealt with in this paper.According to historical data of power output and its influencing factors,a decision table is built up,and discretization of continuous attributes in the table is done by means of dynamic layer cluster.Using the attribute reduction algorithm to eliminate some redundant attributes from the table,the kernel factors are determined.Taking these kernel factors as the attributes of both training and testing samples,the power supply forecasting is conducted.Five-year forecasting results show that,compared with SVR which chooses attributes of input vectors in light of experience,the method of RSSVR could make use of less but cardinal predictors' information,and the forecasting accuracy is improved.