Wv-SVM with genetic algorithms for gas load forecasting

Author

Lai, Zhaolin ; Xu, XiaoZhong

Author_Institution

Inf. & Electr. Eng. Coll., Shanghai Normal Univ., Shanghai, China

Volume

1

fYear

2011

fDate

10-12 June 2011

Firstpage

393

Lastpage

398

Abstract

Accurate short-term load forecasting is important in regional or national gas pipeline network system strategy management. Gas load forecasting is complex to conduct due to its nonlinearity of influenced factors. Support vector machines (SVM) has been successfully employed to solve nonlinear regression and time series problems, however, the application for load forecasting is rare. The purpose of this paper is to present a wavelet v-SVM with genetic algorithm (GA) to forecast the gas loads. GA is applied to the parameter determine of Wv-SVM model. The empirical results reveal that the proposed model outperforms the artificial neural network (ANN), Wv- SVM models.

Keywords

genetic algorithms; load forecasting; neural nets; pipelines; regression analysis; support vector machines; time series; wavelet transforms; GA; Wv-SVM model; artificial neural network; gas load forecasting; genetic algorithm; national gas pipeline network system strategy; nonlinear regression; regional strategy management; short-term load forecasting; support vector machine; time series problem; wavelet v-SVM; Forecasting; Genetic algorithms; Kernel; Load modeling; Predictive models; Support vector machines; Wavelet transforms; Gas load forecasting; genetic algorithms (GA); support vector machines (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-8727-1

Type

conf

DOI

10.1109/CSAE.2011.5953247

Filename

5953247