Electric load combined forecasting model weights optimization using an improved particle swarm algorithm

Authors:
Jiang Chuanwen;Ma Yuchao;Liu Yong;Lu Jianyu;Wang Liang
Affiliations:
Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai, P.R China;Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai, P.R China;Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai, P.R China;East China Grid Company Limited, Shanghai, P.R China;East China Grid Company Limited, Shanghai, P.R China
Venue:
ICC'05 Proceedings of the 9th International Conference on Circuits
Year:
2005

Citing 3
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Abstract

The Electric load series always presents complex phenomenon because of the influence of many complicated facts, various forecasting results can be obtained by using different models for a given electric power utility. The combined forecasting model is recognized as an appreciative method. The paper introduces an improved Particle swarm optimization (PSO) for electric load combination forecasting model weight optimization. The new method applies a self-adaptive weight scale operator to avoid being trapped in the local optimum in conventional Particle swarm optimization. The proposed method has been examined and tested on a practical system. The test results show that the improved PSO has better convergence and faster calculation speed than the basic PSO, and the presented combination forecast model has improved the accuracy.