Forecasting system based on Wavelet Transform and PSO-SVM

Author

Chen, Qisong ; Wu, Yun ; Zhang, Xin ; Chen, Xiaowei

Author_Institution

Sch. of Comput. Sci. & Technol., Guizhou Univ., Guiyang

fYear

2008

Firstpage

305

Lastpage

309

Abstract

A new method for load forecasting based on LS-SVM, PSO and wavelet transform is proposed. The wavelet transform is adopted to decompose the historical data, so the approximate part and several detail parts are obtained. The results of wavelet transform are predicted by a separate LS-SVM predictor. PSO is employed to determine these parameters of SVM model. The novel forecast model integrates the advantage of WT, PSO and LS-SVM. Compared with other predictors, this forecast model has greater generality ability and higher accuracy.

Keywords

forecasting theory; particle swarm optimisation; support vector machines; wavelet transforms; SVM model; load forecasting system; particle swarm optimization; support vector machines; wavelet transform; Autoregressive processes; Computer science; Discrete wavelet transforms; Least squares methods; Load forecasting; Particle swarm optimization; Predictive models; Stochastic processes; Support vector machines; Wavelet transforms; PSO; Wavelet Transform; forecasting; least squares support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Anti-counterfeiting, Security and Identification, 2008. ASID 2008. 2nd International Conference on

Conference_Location

Guiyang

Print_ISBN

978-1-4244-2584-6

Electronic_ISBN

978-1-4244-2585-3

Type

conf

DOI

10.1109/IWASID.2008.4688383

Filename

4688383