关键词: 光伏功率预测, 超短期预测, PatchTST (patch time series transformer), GLAFF (global-local attention fusion framework), 数据漂移

Abstract:

Existing PV power prediction methods often suffer from limited accuracy and robustness due to three key shortcomings: relying on single-point mapping that cannot fully extract local temporal patterns; inadequate exploration of the global temporal dependencies in PV output, and failure to account for prevalent data drift phenomena. To overcome these limitations, an improved patch time series transformer (PatchTST) based approach is proposed for ultra-short-term PV power prediction. The methodology applies rough set theory for feature dimensionality reduction, effectively preserving critical decision information by analyzing both feature-label relationships and inter-feature correlations. An enhanced PatchTST model with a modified channel-independent mechanism extracts local temporal patterns and captures complex mapping relationships between meteorological variables and PV output. The global-local attention fusion framework (GLAFF) module is incorporated to identify global temporal guidance patterns. An adaptive weighting mechanism dynamically optimizes the integration of local and global features to counteract data drift effects. Experimental validation using real-world datasets from a PV power plant in Inner Mongolia, China, and the Desert Knowledge Australia Solar Centre demonstrates that the proposed model achieves superior performance in both prediction accuracy and robustness.

Key words: photovoltaic power prediction, ultra-short-term prediction, patch time series transformer (PatchTST), global-local attention fusion framework (GLAFF), data drift