Adaptive Graph-Learning Convolutional Network for Multi-Node Offshore Wind Speed Forecasting

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