Short-term power load forecasting with least squares support vector machines and Wavelet Transform

被引：4
|
作者：
Chen, Qi-Song [1 ]
Zhang, Xin [2 ]
Xiong, Shi-Huan [3 ]
Chen, Xiao-Wei [1 ]
机构：
[1] Guizhou Univ, Sch Comp Sci & Technol, Guiyang 550025, Peoples R China
[2] GuiZhou Key Lab Photoelect Technol & Applicat, Guiyang 550025, Peoples R China
[3] Guizhou Educ Coll, Math & Comp Sci Dept, Guiyang 550003, Peoples R China
来源：
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2008年
关键词：
Wavelet Transform; least squares support vector machines; short-term; power load forecasting;
D O I：
10.1109/ICMLC.2008.4620629
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
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 (he 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.
引用
收藏
页码：1425 / +
页数：2
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