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

Shijie Xu, Hui Ma, Chandima Ekanayake and Yi Cui

Renewable Energy, 2025, vol. 246, issue C

Abstract: 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.

Keywords: Deep learning; Forecasting; Image processing; Photovoltaic (PV); Vision transformers; Transfer learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:246:y:2025:i:c:s0960148125004860

DOI: 10.1016/j.renene.2025.122824

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