Multiscale Dynamic Graph Network for Forecasting of Wind Speeds in Offshore Regions

Accurate prediction of wind speed in offshore areas is critical to maintaining the reliability and stability of offshore wind power. However, existing graph neural network methods focus only on a single time scale and ignore potential temporal patterns. Moreover, they rely on predefined graph structures and cannot capture dynamic spatial correlations between ocean locations, which affects the representation of offshore wind speed patterns. Therefore, we introduce the Multi-Scale Dynamic Graph Network (MSDGN) to enhance wind speed forecasting accuracy in offshore regions. MSDGN introduces a network with a multi-scale pyramid structure to capture potential time-based dependencies in raw data across various scales, and infers spatial relationships between oceanic locations at specific scales through a self-adapting learning graph network. Subsequently, the proposed temporal graph network with multi-scale concurrently captures both the time-based dependencies in sequence data and the spatial relationships among oceanic locations. Finally, the model integrates a scale-wise fusion module to facilitate cooperation among various scales, further enhancing prediction accuracy. Extensive experiments on Chinese sea data show our model outperforms state-of-the-art methods in multi-node wind speed prediction.