A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting

Abstract

Abstract Accurate short-term wind power forecasting is significant for rational dispatching of the power grid and ensuring the power supply quality. In order to enhance the accuracy of short-term wind speed prediction, a hybrid model based on VMD-WT and PCA-BP-RBF neural network is proposed. In data pre-processing period, the non-stationary wind speed sequence is decomposed into a number of relatively stationary intrinsic mode functions (IMF) by variational mode decomposition (VMD); then WT algorithm is used to perform secondary denoising on each IMF. At the same time, several factors affecting wind speed are introduced, from which the input features that participated in the prediction are selected by PCA-BP method. Next, the RBF neural network is utilized to predict each IMF. Finally, all IMF prediction results are aggregated to obtain the final wind speed value. Combining the data of Spanish and Chinese wind farms, the experiment results show that: (1) compared with EMD, VMD-WT can better solve the problems of modal aliasing and endpoint effect, which can make the periodic characteristics of each IMF more obvious, then promote the forecasting performance; (2) using PCA-BP method to filter the model input data, the redundant and irrelevant information is eliminated, the complexity of the model is reduced, and the predictive performance of RBF model is improved; (3) compared with other traditional models, the hybrid model proposed in this paper has greatly improved the accuracy in short-term wind speed forecasting.