SVM Model Based on Particle Swarm Optimization for Short-Term Load Forecasting

Authors:
Yongli Wang;Dongxiao Niu;Weijun Wang
Affiliations:
Doctor, Institute of Business Management, North China Electric Power University, Beijing, China 102206;Professor, Institute of Business Management, North China Electric Power University, Beijing, China 102206;Doctor, Institute of Business Management, North China Electric Power University, Beijing, China 102206
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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Abstract

A model integrating Particle Swarm Optimization (PSO) and support vector machines (SVM) is presented to forecast short-term load of electric power systems in this paper. PSO is a method for finding a solution of stochastic global optimizer based on swarm intelligence. Using the interaction of particles, PSO searches the solution space intelligently and finds out the best one. The PSO-SVM method proposed in this paper is based on the global optimization of PSO and local accurate searching of SVM. Practical example results indicate that the application of the PSO-SVM method to short term load forecasting of power systems is feasible and effective. And to prove the effectiveness of the model, other existing methods are used to compare with the result of SVM. The results show that the model is effective and highly accurate in the forecasting of short-term power load.