Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey

Yuhao Nie, Xiatong Li, Quentin Paletta, Max Aragon, Andea Scott and Adam Brandt

Renewable and Sustainable Energy Reviews, 2024, vol. 189, issue PB

Abstract: Sky image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty of solar power generation. However, a major challenge is the lack of large quantity of sky image data encompassing diverse sky conditions for model training. This study presents a comprehensive survey of open-source sky image datasets for solar forecasting and related research areas, including cloud segmentation, classification and motion prediction which could potentially enhance solar forecasting capabilities. In total, 72 open-source sky image datasets are identified globally that satisfy the needs of deep learning-based method development. A database containing information about various aspects of the identified datasets is constructed. A multi-criteria ranking system is further developed to evaluate each dataset based on eight dimensions which could have important impacts on the data usage. Finally, insights on the applications of these datasets are provided. This study streamlines the processes of identifying and selecting sky image datasets, and could potentially accelerate the method development and benchmark in solar forecasting and related fields including energy meteorology and atmospheric science.

Keywords: Open-source datasets; Ground-based sky images; Solar irradiance; Photovoltaic power; Solar forecasting; Cloud segmentation; Cloud classification; Cloud motion prediction; Deep learning (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.rser.2023.113977

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