Multi-node wind speed forecasting is greatly important for offshore wind power. It is a challenging task due to unknown complex spatial dependencies. Recently, graph neural networks (GNN) have been applied to wind forecasting because of their capability in modeling dependencies. However, existing methods usually require a pre-defined graph structure, which is not optimal for the downstream task and limits the application scope of GNN. In this paper, we propose adaptive graph-learning convolutional networks (AGLCN) that can automatically infer hidden associations among multi-nodes through a graph-learning module. It simultaneously integrates the temporal and graph convolutional modules to capture temporal and spatial features in the data. Experiments are conducted on real-world multi-node wind speed data from the China Sea. The results show that our model achieves state-of-the-art results in all multi-scale wind speed predictions. Moreover, the learned graph can reveal spatial correlations from a data-driven perspective.

Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction.