标题：A Modified Forecasting Algorithm for Wind Power Based on SVM
作者：Xiao, Wenwen; Sun, Ying; Kejun; Li; Xu, Mi; Li, Hao; Yu, Lin; Gao, Liyuan
作者机构：[Xiao, Wenwen; Sun, Ying; Kejun; Li; Xu, Mi; Li, Hao; Yu, Lin; Gao, Liyuan] Shandong Univ, Sch Elect Engn, Jinan 250061, Shandong, Peoples R China.
会议名称：IEEE Region 10 Conference
会议日期：NOV 01-04, 2015
来源：TENCON 2015 - 2015 IEEE REGION 10 CONFERENCE
关键词：classification; large sample; regression; SVM; size optimization; wind; power forecasting
摘要：Aiming at the saturation characteristic of SVM in large sample environment, a modified SVM forecasting algorithm for wind power forecasting is proposed in this paper. The key point of the modified SVM forecasting algorithm is converting large sample set to small sample set by making classification. In this method, the optimal regression size for SVR is firstly sought out for the actual sample, and then the training samples are divided into several categories according to wind power output with different class labels. Based on SVC, train out classification model; based on SVR, regression model of each class can be built. Forecast data of wind power can be obtained by taking the text data into above classification model and corresponding regression model. At last, the proposed algorithm is applied to a wind farm of Shandong Province; and the results verify its validity and effectiveness.