By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Qi-Song Chen ; Sch. of Comput. Sci. & Technol., Guizhou Univ., Guiyang ; 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:

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

Date of Conference:

12-15 July 2008