Short-Term Wind Power Prediction Based on Wavelet Feature Arrangement and Convolutional Neural Networks Deep Learning

Abstract

Wind power prediction (WPP) has an important impact on the security and reliability operation of power grid after a large amount of wind power integration into the system. There are two main challenges in WPP: 1) Both numerical weather prediction (NWP) and wind power contain abundant frequency information. If these data are directly input to the prediction model, the connection between different frequency bands is difficult to be mined. 2) Wind power has strong randomness and volatility, so the nonlinear relationship between input and output is difficult to be reflected by traditional prediction models. To overcome the challenge, a novel short-term WPP model based on wavelet feature arrangement and convolutional neural networks (CNN) is proposed in the article. First, wavelet transform is applied to split the original NWP data and historical power data into multiple sets of different frequency components. Then, the features of different frequencies are arranged in various ways, named feature arrangement (FA), which are input into the CNN model for WPP, and finally, the prediction results are obtained. Two case studies demonstrated the effectiveness of the proposed novel WT-FA-CNN deep learning model for short-term WPP.