DocumentCode :
2556404
Title :
Support vector machine with PSO algorithm in short-term load forecasting
Author :
Rong, Gao ; Xiaohua, Liu
Author_Institution :
Shool of Math. & Inf., Ludong Univ., Yantai
fYear :
2008
fDate :
2-4 July 2008
Firstpage :
1140
Lastpage :
1142
Abstract :
Support vector machines (SVM) have been successfully employed to solve nonlinear regression and time series problem. In this paper SVM and particle swarm optimization (PSO) have been employed to forecast electricity load. PSO algorithm was employed to choose the parameters of a SVM. Subsequently, examples of electricity load data from Shandong electric company were used to illustrate the proposed method. The result reveal that the proposed method was effective.
Keywords :
load forecasting; particle swarm optimisation; power engineering computing; support vector machines; Shandong electric company; electricity load forecasting; particle swarm optimization; short-term load forecasting; support vector machine; Abstracts; Load forecasting; Mathematics; Particle swarm optimization; Predictive models; Support vector machines; load forecasting; particle warm optimization; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-1733-9
Electronic_ISBN :
978-1-4244-1734-6
Type :
conf
DOI :
10.1109/CCDC.2008.4597492
Filename :
4597492
Link To Document :
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