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An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.

A new hybrid particle swarm optimization (PSO) algorithm with adaptive inertia weight factor (AIWF) is proposed. By incorporating chaotic local research method, it proposed the PSO which combined with chaos (CPSO), and applied it in evolving the artificial neural network (ANN). Then, based on the actual load data provided by a regional power grid in the south of China, the proposed method is used in the load forecasting. Results and comparisons with the PSO-ANN and the GA-ANN algorithms show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality. While being used in the short-term load forecasting, the CPSO-ANN is better than the other algorithms in both forecasting effect and network function, such as PSO-ANN, GA-ANN and so on.