Improved wavelet neural network combined with particle swarm optimization algorithm and its application^①

来源期刊：中南大学学报(英文版)2006年第3期

论文作者：李翔 杨尚东 乞建勋 杨淑霞

文章页码：256 - 259

Key words：artificial neural network; particle swarm optimization algorithm; short-term load forecasting; wavelet;curse of dimensionality

Abstract: 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.

基金信息：the National Natural Science Foundation of China