Regional Day‐Ahead Wind Power Forecasting via Multi‐Scale NWP Representation and Operational Priors

ABSTRACT

Accurate regional day‐ahead wind power forecasting is important for renewable energy integration and power system scheduling. However, existing methods often insufficiently exploit multi‐scale spatial information in high‐dimensional multi‐grid numerical weather prediction (NWP) data, and station‐level operating information available before forecasting is rarely represented explicitly. To address these limitations, we propose a multi‐branch forecasting model that integrates multi‐scale NWP representation with station‐level operating priors. The proposed model extracts local spatial features, learns extended‐area meteorological representations through a variational autoencoder and constructs adaptive station‐level neighbourhood features for regional aggregated wind power forecasting. Maintenance reduction factors and curtailment constraint factors are further embedded to characterise changes in station‐level output boundaries under planned operating constraints. The model is evaluated using two real‐world wind farm clusters and compared with representative machine‐learning and deep‐learning benchmarks. Results show that the proposed model achieves normalised mean absolute error (NMAE), normalised root mean square error (NRMSE) and R ² values of 0.0686, 0.0946 and 0.6656 on Cluster 1, and 0.0795, 0.1101 and 0.7813 on Cluster 2, respectively. Ablation results indicate that station‐level operating priors, adaptive neighbourhood extraction and multi‐scale NWP representation contribute to improved forecasting performance.