Application of SVM and wavelet neural network method for short-term power load forecasting

Author

Zhang, Qian ; Liu, Tongna

Author_Institution

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

Volume

2

fYear

2010

fDate

26-28 Feb. 2010

Firstpage

412

Lastpage

416

Abstract

This paper put forward a new method of the SVM and wavelet neural network 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; radial basis function networks; support vector machines; fuzzy rules; short-term electric load forecasting; short-term power load forecasting; support vector machine; wavelet neural network method; Artificial neural networks; Economic forecasting; Energy management; Estimation error; Load forecasting; Neural networks; Power generation economics; Predictive models; Risk management; Support vector machines; SVM; electric Load Forecasting; fuzzy rules;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-5585-0

Electronic_ISBN

978-1-4244-5586-7

Type

conf

DOI

10.1109/ICCAE.2010.5451579

Filename

5451579