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Dataset for Machine Learning: Explicit All-Sky Image Features to Enhance Solar Irradiance Prediction

Author

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  • Joylan Nunes Maciel

    (Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration–UNILA, Paraná City 85867-000, Brazil
    Postgraduate Program in Applied Physics (PPGFISA), Federal University of Latin American Integration–UNILA, Paraná City 85867-000, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE), Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, Brazil)

  • Jorge Javier Gimenez Ledesma

    (Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration–UNILA, Paraná City 85867-000, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE), Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, Brazil)

  • Oswaldo Hideo Ando Junior

    (Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration–UNILA, Paraná City 85867-000, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE), Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, Brazil
    Program in Energy Systems Engineering (PPGESE), Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, Brazil)

Abstract

Prediction of solar irradiance is crucial for photovoltaic energy generation, as it helps mitigate intermittencies caused by atmospheric fluctuations such as clouds, wind, and temperature. Numerous studies have applied machine learning and deep learning techniques from artificial intelligence to address this challenge. Based on the recently proposed Hybrid Prediction Method (HPM), this paper presents an original and comprehensive dataset with nine attributes extracted from all-sky images developed using image processing techniques. This dataset and analysis of its attributes offer new avenues for research into solar irradiance forecasting. To ensure reproducibility, the data processing workflow and the standardized dataset have been meticulously detailed and made available to the scientific community to promote further research into prediction methods for photovoltaic energy generation.

Suggested Citation

  • Joylan Nunes Maciel & Jorge Javier Gimenez Ledesma & Oswaldo Hideo Ando Junior, 2024. "Dataset for Machine Learning: Explicit All-Sky Image Features to Enhance Solar Irradiance Prediction," Data, MDPI, vol. 9(10), pages 1-12, September.
    • Handle: RePEc:gam:jdataj:v:9:y:2024:i:10:p:113-:d:1488530
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    References listed on IDEAS

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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
    3. Ariana Moncada & Walter Richardson & Rolando Vega-Avila, 2018. "Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset," Energies, MDPI, vol. 11(8), pages 1-16, July.
    4. Williamson, Sarah & Businger, Steven & Matthews, Dax, 2018. "Development of a solar irradiance dataset for Oahu, Hawai'i," Renewable Energy, Elsevier, vol. 128(PA), pages 432-443.
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