Swin transformer-based transferable PV forecasting for new PV sites with insufficient PV generation data

To effectively operate solar farms, accurate photovoltaic (PV) generation forecasting is required. For a newly constructed solar farm (PV site), its generation data could be limited. Using the sky images obtained from ground-based whole-sky cameras, this paper proposes a transferable Double route Shifted window Cross-Attention Transformer (DSCAT) framework to provide PV forecasting of the newly constructed PV site. The framework is trained using the data of an established PV site and then provides ultra-short-term PV forecasting for the newly constructed PV site. In the proposed framework, a temporal difference parallel Shifted window (Swin) Transformer-based structure is designed to capture the cloud motion details and extract the static spatial features. Then, a cross-attention structure is utilized to analyze the temporal features and predict the future PV generation. A variety of transfer strategies are designed to transfer the trained model to provide the PV forecasting at the new PV site. The training and transfer experiments are conducted with real-world sky images and PV generation datasets. The result shows the proposed framework could be transferred between varied environments, and provide a reliable forecast which achieves a 49% enhancement over the persistence baseline and 13% improvement over the PV forecasting benchmarks on average. • A transferable ultra-short-term photovoltaic (PV) forecasting model was proposed. • A double route cross-attention structure utilized for sky image sequences. • Detailed transfer learning strategies are designed and evaluated. • Experiments for PV forecasting and transferring between two real-world datasets.