Short-Term Wind Power Forecasting Using Empirical Mode Decomposition and RBFNN

In order to effectively predict wind farm power with non-linear and non-stationary characteristics, a prediction model based on empirical mode decomposition (EMD) and radial basis function neural networks(RBFNN) was designed. The forecast model uses EMD to decompose the wind power into several intrinsic mode functions (IMF) and one residue. The RBFNN was used to construct a prediction model for each IMF component and the residue, the input variables of the prediction model are triple: wind speed, wind direction, and history wind power. All the prediction results of components were aggregated to obtain the ultimate prediction result. The simulation results show that compared with the traditional prediction method based on artificial neural networks, this method has high prediction precision and strong adaptability.