Vision transformer models to measure solar irradiance using sky images in temperate climates

Thomas M. Mercier, Amin Sabet and Tasmiat Rahman

Applied Energy, 2024, vol. 362, issue C, No S0306261924003507

Abstract: Solar Irradiance measurements are critical for a broad range of energy systems, including evaluating performance ratios of photovoltaic systems, as well as forecasting power generation. Using sky images to evaluate solar irradiance, allows for a low-cost, low-maintenance, and easy integration into Internet-of-things network, with minimal data loss. This work demonstrates that a vision transformer-based machine learning model can produce accurate irradiance estimates based on sky-images without any auxiliary data being used. The training data utilizes 17 years of global horizontal, diffuse and direct data, based on a high precision pyranometer and pyrheliometer sun-tracked system; in-conjunction with sky images from a standard lens and a fish-eye camera. The vision transformer-based model learns to attend to relevant features of the sky-images and to produce highly accurate estimates for both global horizontal irradiance (RMSE =52 W/m2) and diffuse irradiance (RMSE = 31 W/m2). This work compares the model’s performance on wide field of view all-sky images as well as images from a standard camera and shows that the vision transformer model works best for all-sky images. For images from a normal camera both vision transformer and convolutional architectures perform similarly with the convolution-based architecture showing an advantage for direct irradiance with an RMSE of 155 W/m2.

Keywords: Computer vision; Machine learning; Solar irradiance; Sky imaging (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2024.122967

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