By Topic

Short-term load forecasting using multiple support vector machines based on fuzzy clustering

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

2 Author(s)
Gaorong ; Shool of Math. & Inf., LuDong Univ., Yantai, China ; Liu Xiao-hua

According to the future of power load, a load forecasting method of multiple support vector machine based on fuzzy clustering is proposed. Data type, weather and temperature factors are considered in the model. Load data are classified using fuzzy clustering. Each class was modeled using support vector machines which best fitted the special class. The method was simulated utilizing the load data of Shan Dong electrical company from 2005 to 2007. The simulation result showed our method can improve the forecasting accuracy.

Published in:

Control and Decision Conference, 2009. CCDC '09. Chinese

Date of Conference:

17-19 June 2009