An Improved Data‐Driven Spatio‐Temporal Downscaling Framework for Photovoltaic Power Prediction

Photovoltaic (PV) power prediction methods often exhibit limited accuracy because they rely on low‐resolution numerical weather prediction data that fails to capture microclimate characteristics. To address it, this study proposes a hybrid data‐driven PV prediction framework built around a novel Meteorological Spatial Downscaling Module (MSDM). It first systematically extracts key data features through a Spatio‐Temporal Feature Engineering Module (ST‐FEM). Subsequently, within the MSDM, an Improved Spatial Averaging‐Based Meteorological Field Synthesis (I‐SAMS) method is employed to effectively fuse high‐quality, real‐time meteorological data from neighboring reference stations, generating a high‐resolution, localized meteorological field for the target station. The experimental results demonstrate that the proposed ST‐FEM+I‐SAMS framework achieves significant improvements in prediction performance, reducing the normalized Root Mean Square Error (nRMSE) and normalized Mean Absolute Error (nMAE) by 54.95% and 59.32%, respectively, compared to the baseline model without spatial information. Furthermore, it exhibits robustness under highly variable meteorological conditions. Therefore, these findings indicate that the I‐SAMS method is appropriate for integration with ST‐FEM, which effectively utilizes complex data from neighboring stations as virtual meteorological sensors, providing a cost‐effective, data‐driven solution to improve PV power prediction accuracy.