By Topic

A Novel Hybrid GA Based SVM Short Term Load Forecasting Model

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

1 Author(s)
Wei Sun ; Dept. of Econ. & Manage., North China Electr. Power Univ., Baoding, China

The increasing importance and complexity of STLF necessitates more accurate load forecast methods. A novel genetic algorithm (GA) based support vector machine (SVM) forecasting model with determinstic annealing (DA) clustering is presented in this paper. For NN forecasting, too many training data may lead to long training time and slow convergent speed. First deterministic annealing (DA)for load data clustering technique is adopted first to solve the problem. After data clustering, GA based SVM forecasting model is established. The parameters for SVM were optimized through genetic algorithms, which were used in SVM model. The hibrid GA-SVM forecasting model is tested by using Hebei Province practical load data. The experimental results demonstrate the GA-SVM model outperforms the BP neural network model based on the root mean square error (RMSE) and the mean absolute percentage error (MAPE). And the proposed method provided a satisfactory improvement of forecasting accuracy.

Published in:

Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on  (Volume:2 )

Date of Conference:

Nov. 30 2009-Dec. 1 2009