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

1 INTRODUCTIONFor the last decade ,the wavelet neural net-work ( WNN) method was noticed by many re-searchers1 3].It has been widely appliedin variousaspects such as short term load forecasting4 ,5].While ,it is prone to cause the curse of di mension-ality with the factors taken into consideration in-creasing , which becomes the bottleneckfor thei m-provement of its application6 ,7].Inthis study ,a new methodfor opti mizingthestructure of wavelet networks was developed byadopting an p…

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

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.