Dynamic adaptive spatio-temporal graph neural network for multi-node offshore wind speed forecasting

Abstract

Multi-node offshore wind speed forecasting is a challenging task due to the complex dynamic spatial dependencies and highly nonlinear temporal dynamics present in the ocean. As deep learning advances, graph neural networks (GNNs) have great potential to capture spatial dependencies in ocean meteorology. However, existing GNN models usually use predefined or learned static graphs. They lack the ability to model dynamic spatial associations, which can limit the performance of GNNs. In this paper, we propose a dynamic adaptive spatio-temporal graph neural network (DASTGN) that uses dynamic graph convolution (DGCN) to capture dynamic spatial dependencies in offshore wind speed data. Based on the assumption that not only long-term static associations but also short-term dynamic associations exist in the spatial domain and that the importance of these two associations is different, we propose a dynamic adaptive graph generation module to generate static and dynamic graphs to model these two associations. Meanwhile, a matrix fusion mechanism is proposed to fuse them into the optimal dynamic graph, which is fed into the DGCN module. We employ a temporal convolution module to capture the nonlinear temporal dependencies. Finally, the above modules are integrated into a dedicated spatio-temporal convolution module to predict wind speed. Extensive experiments on real wind speed datasets in Chinese seas showed that the DASTGN improved the performance of the optimal baseline model by 3.05% and 3.69% in terms of the MAE and RMSE, respectively. To demonstrate that the DASTGN can effectively model dynamic spatial associations, the generated graph structure is visualized and analyzed. Finally, we present policy implications aimed at enhancing the security of the power system.