SHORT-TERM POWER LOAD FORECASTING WITH LEAST SQUARES SUPPORT VECTOR MACHINES AND WAVELET TRANSFORM

摘要：

Based on least squares support vector machines (LS-SVM) and Wavelet Transform theory, a novel approach for short-term power load forecasting is presented. The historical time series is decomposed by wavelet, so the approximate part and several detail parts are obtained. Then the results of Wavelet Transform are predicted by a separate LS-SVM predictor. The new forecast model combines the advantage of WT with LS-SVM. Compared with other predictors, this forecast model has greater generalizing ability and higher accuracy.

关键词： Wavelet Transform least squares support vector machines short-term power load forecasting

作者: QI-SONG CHEN XIN ZHANG SHI-HUAN XIONG XIAO-WEI CHEN

作者单位: School of Computer Science and Technology, Guizhou University, Guiyang 550025, China GuiZhou Key Laboratory for Photoelectric Technology and Application, Guiyang, 550025, China Mathematics and Computer Science Department of Guizhou Education College, Guiyang, 550003, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1425-1429

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）