Short-Term Forecasting Based on Graph Convolution Networks and Multiresolution Convolution Neural Networks for Wind Power

Accurate prediction of wind power generation is of great significance for the efficient operation of wind farms. However, traditional deep learning-based methods predict the wind power without simultaneously considering the temporal features of wind power and spatial features between variables, which leads to low prediction accuracy. This article proposes a novel wind power forecasting approach based on a graph convolution network (GCN) and a multiresolution convolution neural network (CNN), combining spatial features and temporal features. In this approach, GCN merged with maximum information coefficient (MIC) is proposed to extract the spatial correlation features between input variables, which considers the effects of multiple variables on wind power and provides interpretability for deep learning-based forecasting. On the other hand, multiresolution CNN combines multiscale convolution kernels with a new self-attention mechanism to understand local and long-term temporal features, which enables simultaneous prediction of wind power and other variables. Experiments on a real dataset prove that the proposed method is effective and accurate in short-term wind power forecasting. Comparisons with the other three state-of-the-art methods and ablation experiments also reveal the advantages of the proposed approach.