Spatial‐Awared Deep Echo State Networks for High‐Resolution Spatio‐Temporal Wind Speed Nowcasting

Accurate wind speed nowcasting is crucial for optimizing wind energy production and grid stability, especially for countries such as Saudi Arabia with ambitious renewable energy targets. This article introduces a novel deep learning framework combining Deep Echo State Networks (DESNs) with spatial information through k$$ k $$‐nearest neighbors for high‐resolution wind speed prediction. Specifically, we develop a Spatio‐Temporal Attention Graph Autoencoder (STAGA) that effectively reduces spatial dimensionality while preserving critical spatio‐temporal patterns, enabling efficient processing of large‐scale meteorological data. Experiments using high‐resolution simulated wind data from Saudi Arabia demonstrate that our model consistently outperforms traditional methods. In addition, we provide uncertainty quantification through conformal prediction and demonstrate its practical value by assessing wind power estimates. The proposed methodology offers significant improvements for operational wind forecasting systems, supporting the efficient integration of wind energy into power grids.