Wind Power Forecast Using Multilevel Adaptive Graph Convolution Neural Network

Accurate forecasting of wind power is essential for maintaining the stability and efficiency of power networks as renewable energy sources become more integrated. This study proposes a multi-level spatial–temporal graph convolution network (MLAGCN) that combines a multi-level adaptive graph convolution (MLAGC) and a temporal transformer module (TTM) for wind power forecasting. Specifically, MLAGC first extracts spatial representations for each turbine at every time step by dynamically modeling local, global, and structural interactions. These spatial embeddings are then organized as temporal sequences and fed into TTM, which captures both short-term fluctuations and long-term temporal dependencies via self-attention. MLAGC is constructed using three adaptive graphs: a local-aware graph, a global-aware graph, and a structure-aware graph. These components form a flexible graph structure that effectively represents dynamic spatial interactions, while TTM learns short- and long-term sequential patterns. Experiments on real wind farm datasets demonstrated that the proposed model outperforms existing baselines. The model achieved improved prediction accuracy and generalization, as indicated by a lower composite score (defined as the average of MAE and RMSE) of 43.44, and a forecast loss of 0.22. These results demonstrate the effectiveness of temporal modeling and multi-level attention-based adaptive graph learning for high-resolution wind power forecasting.