Spatio‐Temporal Dual‐Encoder Transformer for Short‐Term Regional Wind Power Forecasting

ABSTRACT Accurate regional wind power forecasting (RWPF) is critical for grid integration and stability, yet remains challenging due to the inherently complex spatio‐temporal dependencies among geographically distributed wind farms. This paper introduces the spatio‐temporal dual‐encoder Transformer (ST‐DualFormer), an architecture designed to improve the accuracy of short‐term RWPF. ST‐DualFormer utilizes two parallel encoder streams to separately model temporal and spatial dependencies from meteorological and historical power data. In contrast to graph‐based models that rely on predefined spatial connections, ST‐DualFormer leverages the attention mechanism to capture comprehensive and dynamic correlations across all wind farms in a fully connected manner, facilitating flexible and comprehensive spatio‐temporal correlations. Evaluated on real‐world data from 28 wind farms, ST‐DualFormer achieves a normalized mean absolute error (nMAE) of 5.25% and a normalized root mean squared error (nRMSE) of 7.53% for three‐day‐ahead forecasting, outperforming the tested graph‐based and Transformer‐based baselines. Additional validation on two Weather2K‐R regional subsets further provides initial evidence that the dual‐stream architecture can transfer beyond the original study region.