Physics‐Aware Gated Spatiotemporal Fusion Network for Adaptive Large‐Scale Atmospheric Circulation Guidance in Wind Speed Prediction

Abstract

Accurate spatiotemporal wind speed prediction is essential for improving wind energy utilization and ensuring power grid security. However, existing methods often rely on incorporating multiple meteorological variables, which increases model complexity and obscures the role of large‐scale circulation in guiding local wind evolution. This study proposes a physics‐aware gated spatiotemporal fusion network (PAG‐STFN) for 24‐hr wind speed prediction over southern China and its coastal regions. By introducing mean sea level pressure to represent the large‐scale circulation background, the model adaptively modulates historical wind field features. Results show that PAG‐STFN yields smaller mean errors than the baseline models, with particularly clear advantages at lead times of 6 hr and beyond. The model also preserves the seasonal wind field structures over the study region and demonstrates good predictive performance at offshore locations near Jinwan and Changle. Analysis of a cold wave event further illustrates that PAG‐STFN can adaptively regulate the intensity and spatial location of the injected background information, thereby contributing to improved prediction accuracy. Meanwhile, PAG‐STFN introduces only a marginal increase in model parameters while maintaining fast inference, highlighting its practical potential in wind power prediction and operational dispatch.