Spatial correlation learning based on graph neural network for medium-term wind power forecasting

With the increasing penetration of wind power in power grid, accurate and reliable wind power forecasting is of great significance for the economic operation and safe dispatching of electrical power system. In practice, there exists complex spatial correlation between wind power variables, which brings great challenges to the accurate forecasting of wind power. However, traditional deep learning-based methods mostly focus on temporal feature while ignoring the spatial correlation between wind power variables, leading to low forecasting accuracy. To explore the spatial correlation among wind power variables and extract temporal features simultaneously, we propose a double attention-based spatial–temporal neural network (DA-STNet). First, graph attention network is employed to explore the spatial correlation between wind power variables based on a graph constructed by maximal information coefficient, which can consider the combined influences of multivariate on the wind power output. Then, by incorporating causal reasoning and data-driven element-wise attention measures, a novel temporal attention layer is proposed to extract the temporal feature of wind power sequences. Comprehensive experiments were conducted on one self-collected and one public dataset with three different multi-steps ahead forecasting tasks, and the experimental results demonstrated that the performance of proposed DA-STNet is superior to the existing methods on both real-world datasets. In the 24 h ahead experiment on the NWWPF dataset, the MSE of our model can reach as low as 0.136 and MAE can be decreased to 0.275.