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Short-term power load forecasting with least squares support vector machines and wavelet transform

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4 Author(s)
Qi-Song Chen ; School of Computer Science and Technology, Guizhou University, Guiyang 550025, China ; Xin Zhang ; Shi-Huan Xiong ; Xiao-Wei Chen

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.

Published in:

2008 International Conference on Machine Learning and Cybernetics  (Volume:3 )

Date of Conference:

12-15 July 2008