TransPVP: A Transformer-Based Method for Ultra-Short-Term Photovoltaic Power Forecasting

Abstract

The increasing adoption of renewable energy, particularly photovoltaic (PV) power, has highlighted the importance of accurate PV power forecasting. Despite advances driven by deep learning (DL), significant challenges remain, particularly in capturing the long-term dependencies essential for accurate forecasting. This study presents TransPVP, a novel transformer-based methodology that addresses these challenges and advances PV power forecasting. TransPVP employs a deep fusion technique alongside a multi-task joint learning framework, effectively integrating heterogeneous data sources and capturing long-term dependencies. This innovative approach enhances the model’s ability to detect patterns of PV power variation, surpassing the capabilities of traditional models. The effectiveness of TransPVP was rigorously evaluated using real data from a PV power plant. Experimental results showed that TransPVP significantly outperformed established baseline models on key performance metrics including RMSE, R2, and CC, underscoring its accuracy, predictive power, and reliability in practical forecasting scenarios.