Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 12:00 PM ET (12:00 - 16:00 UTC). We apologize for the inconvenience.
By Topic

Short-term load forecast based on fuzzy wavelet support vector machines

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

3 Author(s)
Yuancheng Li ; Digital Media Laboratory, BeiHang Univ., Beijing, China ; Bo Li ; Tingjian Fang

Based on the theory of multiresolution analysis of wavelet transforms and fuzzy concepts, a new method called fuzzy wavelet support vector machines (FWSVM) was presented. The FWSVM consists of a set of fuzzy rules. Each rules corresponding to a sub-wavelet support vector machines (WSVM) with different resolution. Thus the sub-WSVM at different dilation value under these fuzzy rules is fully utilized to capture various essential components of the system. The role of the fuzzy set is to determine the contribution of the sub-WSVM to the output of the FWSVM. Through adjusting the parameters of membership functions, the model accuracy and the generalization capability of the FWSVM can be improved. Analysis of the experimental results proved that FWSVM could achieve greater accuracy than the standard SVM.

Published in:

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on  (Volume:6 )

Date of Conference:

15-19 June 2004