Least Squares Support Vector Machines With Wavelet Transform for Power Load Forecasting

被引：0
|
作者：
Chen Qisong [1 ]
Wu Yun [1 ]
Chen Xiaowei [1 ]
Zhang Xin
机构：
[1] Guizhou Univ, Sch Comp Sci & Technol, Guiyang 550025, Guizhou Prov, Peoples R China
来源：
ICCSE 2008: PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION: ADVANCED COMPUTER TECHNOLOGY, NEW EDUCATION | 2008年
关键词：
power load forecasting; short-term; Wavelet Transform; Genetic Algorithm; least squares support vector machines;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A new method for power load forecasting based on least squares support vector machines (LS-SVM) and Wavelet Transform theory is proposed. The wavelet transform is adopted to decompose the historical data, so the approximate part and several detail parts are obtained. The results of Wavelet Transform are predicted by a separate LS-SVM predictor. LS-SVM is a kind of SVM that has different cost function with respect to standard SVM. Genetic Algorithm is adopted to optimization of these parameters. The novel forecast model integrates the advantage of WT, GA and LS-SVM. Compared with other predictors, this forecast model has greater generality ability and higher accuracy.
引用
收藏
页码：1181 / 1186
页数：6
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