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Machine Learning for Solar Resource Assessment Using Satellite Images

Author

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  • Luis Eduardo Ordoñez Palacios

    (Escuela de Ingeniería de Sistemas y Computación (EISC), Facultad de Ingeniería, Universidad del Valle, Cali 760001, Colombia)

  • Víctor Bucheli Guerrero

    (Escuela de Ingeniería de Sistemas y Computación (EISC), Facultad de Ingeniería, Universidad del Valle, Cali 760001, Colombia)

  • Hugo Ordoñez

    (Departamento de Sistemas, Facultad de Electrónica y Telecomunicaciones, Universidad del Cauca, Popayán 190001, Colombia)

Abstract

Understanding solar energy has become crucial for the development of modern societies. For this reason, significant effort has been placed on building models of solar resource assessment. Here, we analyzed satellite imagery and solar radiation data of three years (2012, 2013, and 2014) to build seven predictive models of the solar energy obtained at different altitudes above sea level. The performance of four machine learning algorithms was evaluated using four evaluation metrics, MBE, R 2 , RMSE, and MAPE. Random Forest showed the best performance in the model with data obtained at altitudes below 800 m.a.s.l. The results achieved by the algorithm were: 4.89, 0.82, 107.25, and 41.08%, respectively. In general, the differences in the results of the machine learning algorithms in the different models were not very significant; however, the results provide evidence showing that the estimation of solar radiation from satellite images anywhere on the planet is feasible.

Suggested Citation

  • Luis Eduardo Ordoñez Palacios & Víctor Bucheli Guerrero & Hugo Ordoñez, 2022. "Machine Learning for Solar Resource Assessment Using Satellite Images," Energies, MDPI, vol. 15(11), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3985-:d:826397
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    References listed on IDEAS

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    1. Ajith, Meenu & Martínez-Ramón, Manel, 2021. "Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data," Applied Energy, Elsevier, vol. 294(C).
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    Cited by:

    1. Zoltan Varga & Ervin Racz, 2022. "Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System," Energies, MDPI, vol. 15(19), pages 1-18, October.
    2. Jiandong Liu & Yanbo Shen & Guangsheng Zhou & De-Li Liu & Qiang Yu & Jun Du, 2023. "Calibrating the Ångström–Prescott Model with Solar Radiation Data Collected over Long and Short Periods of Time over the Tibetan Plateau," Energies, MDPI, vol. 16(20), pages 1-16, October.

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