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《Proceedings of the Chinese Society of Universities for Electric Power System and its Automation》 2008-03
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Hybrid Optimization Method Based on Evolutionary Algorithm and Particle Swarm Optimization for Short-term Load Forecasting

WANG Bo1,TAI Neng-ling1,ZHAI Hai-qing2,YE Jian2,ZHU Jia-dong3,QI Liang-bo3(1.Department of Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;2.Dispatching and Communication Center of Shanghai Electric Power Company,Shanghai 200350,China;3.Shanghai Meteorologic Center,Shanghai 200030,China)  
Owing to the inherent nonlinear characteristics of the power system loads,solutions of the gradient search based technique may stall at the local optimal points,which lead to high forecasting error.A hybrid method based on evolutionary algorithm and particle swarm optimization(HPSO) to identify the auto-regressive and moving average with exogenous variables(ARMAX) is proposed.HPSO introduces evolutionary algorithm into particle swarm optimization(PSO),thus can get higher precision and faster convergence speed without changing the structure of PSO. The proposed method has been tested on the Shanghai power system,and the results show that the proposed approach can achieve great accuracy.
【CateGory Index】: TM715
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