CORRELATION COEFFICIENT METHOD FOR SUPPORT VECTOR MACHINE INPUT SAMPLES

摘要：

When the support vector machine is used for load forecasting, the input samples of support vector machine have important effect to forecasting results. Support vector machine can study any non-linear relation, but if a group of non-distinct variables are selected as input variable set, the training time of support vector machine is lengthened and the errors become bigger. The non-linear relation of the load can be effectively explained only when a group of appropriate input variables are found. In this paper, the correlation coefficient idea is used to input variables selection of support vector machine short-term load forecasting model. The load values, which have bigger correlation coefficient with expectation output values, are chosen from effect factor sets as input variables. By mean of this method, a preferable input variables set can be gained, the correlation between the input variables and the forecasting points are bigger, and the forecasting results is more exact. The simulation results show that the method is effective.

关键词： Support vector machine input variables selection short-term load forecasting

作者: KUI-HE YANG GAN-LIN SHAN LING-LING ZHAO

作者单位: Department of Optics and Electron Engineering, Ordnance Engineering College, Shijiazhuang 050003, Ch Department of Optics and Electron Engineering, Ordnance Engineering College, Shijiazhuang 050003, Ch College of Information, Hebei University of Science and Technology, Shijiazhuang 050054, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2857-2861

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）