System Maintenance:
There may be intermittent impact on performance while updates are in progress. We apologize for the inconvenience.
By Topic

Input dimension reduction for load forecasting based on 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

4 Author(s)
Xu Tao ; Electr. Power Eng., North China Electr. Power Univ., Beijing, China ; He Renmu ; Wang Peng ; Xu Dongjie

The traditional methods for load forecasting can not supply the required accuracy for the engineering application because we only get limited history data sets and the factors that affect the load forecasting are complex. This paper presents a new framework for the power system short-term load forecasting: firstly, this paper establishes the feature selection model and uses floating search method to find the feature subset; then this paper makes use of the support vector machines to forecast the load and takes full advantage of the SVM to solve the problem with small sample and nonlinear. Hence the accuracy of the estimation result is improved and a better generalization ability is guaranteed. The EUNITE network is employed to demonstrate the validity of the proposed approach.

Published in:

Electric Utility Deregulation, Restructuring and Power Technologies, 2004. (DRPT 2004). Proceedings of the 2004 IEEE International Conference on  (Volume:2 )

Date of Conference:

5-8 April 2004