Wind Power Forecasting Using Wavelet Decomposition and Elman Neural Network

Abstract

The relevant data sequences provided by numerical weather prediction are decomposed into different frequency bands by using the wavelet decomposition for wind power forecasting. The Elman neural network models are established at different frequency bands respectively, then the output of different networks are combined to get the eventual prediction result. For comparison, Elman neutral network and BP neutral network are used to predict wind power directly. Several error indicators are given to evaluate prediction results of the three methods. The simulation results show that the Elman neural network can achieve good results and that prediction accuracy can be further improved by using the wavelet decomposition simultaneously.