Application of Support Vector Machine and Fuzzy Rules Method for Power Load Forecasting

Author

Zhang, Qian

Author_Institution

Dept. of Economic Manage., North China Electr. Power Univ., Baoding, China

Volume

1

fYear

2010

fDate

19-21 March 2010

Firstpage

542

Lastpage

545

Abstract

This paper put forward a new method of the SVM and fuzzy rules model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of SVM. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective way to forecast short-term electric load.

Keywords

fuzzy set theory; load forecasting; power engineering computing; support vector machines; wavelet transforms; SVM; fuzzy rules method; neural call function; nonlinear wavelets; power load forecasting model; short-term electric load forecasting; support vector machine; Application software; Artificial neural networks; Computer applications; Estimation error; Load forecasting; Power engineering and energy; Power engineering computing; Predictive models; Risk management; Support vector machines; SVM; electric load forecasting; fuzzy rules;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Engineering and Applications (ICCEA), 2010 Second International Conference on

Conference_Location

Bali Island

Print_ISBN

978-1-4244-6079-3

Electronic_ISBN

978-1-4244-6080-9

Type

conf

DOI

10.1109/ICCEA.2010.110

Filename

5445771