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Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications

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  • David Puga-Gil

    (Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, España)

  • Gonzalo Astray

    (Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, España)

  • Enrique Barreiro

    (Universidade de Vigo, Departamento de Informática, Escola Superior de Enxeñaría Informática, 32004 Ourense, España)

  • Juan F. Gálvez

    (Universidade de Vigo, Departamento de Informática, Escola Superior de Enxeñaría Informática, 32004 Ourense, España)

  • Juan Carlos Mejuto

    (Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, España)

Abstract

Global solar irradiation is an important variable that can be used to determine the suitability of an area to install solar systems; nevertheless, due to the limitations of requiring measurement stations around the entire world, it can be correlated with different meteorological parameters. To confront this issue, different locations in Rias Baixas (Autonomous Community of Galicia, Spain) and combinations of parameters (month and average temperature, among others) were used to develop various machine learning models (random forest -RF-, support vector machine -SVM- and artificial neural network -ANN-). These three approaches were used to model and predict (one month ahead) monthly global solar irradiation using the data from six measurement stations. Afterwards, these models were applied to seven different measurement stations to check if the knowledge acquired could be extrapolated to other locations. In general, the ANN models offered the best results for the development and testing phases of the model, as well as for the phase of knowledge extrapolation to other locations. In this sense, the selected ANNs obtained a mean absolute percentage error (MAPE) value between 3.9 and 13.8% for the model development and an overall MAPE between 4.1 and 12.5% for the other seven locations. ANNs can be a capable tool for modelling and predicting monthly global solar irradiation in areas where data are available and for extrapolating this knowledge to nearby areas.

Suggested Citation

  • David Puga-Gil & Gonzalo Astray & Enrique Barreiro & Juan F. Gálvez & Juan Carlos Mejuto, 2022. "Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications," Mathematics, MDPI, vol. 10(24), pages 1-21, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4746-:d:1003153
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    References listed on IDEAS

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    1. Mubiru, J. & Banda, E.J.K.B., 2012. "Monthly average daily global solar irradiation maps for Uganda: A location in the equatorial region," Renewable Energy, Elsevier, vol. 41(C), pages 412-415.
    2. Djandja, Oraléou Sangué & Salami, Adekunlé Akim & Wang, Zhi-Cong & Duo, Jia & Yin, Lin-Xin & Duan, Pei-Gao, 2022. "Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 245(C).
    3. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    4. Raquel Fernández-González & Andrés Suárez-García & Miguel Ángel Álvarez Feijoo & Elena Arce & Montserrat Díez-Mediavilla, 2020. "Spanish Photovoltaic Solar Energy: Institutional Change, Financial Effects, and the Business Sector," Sustainability, MDPI, vol. 12(5), pages 1-18, March.
    5. Yacef, R. & Benghanem, M. & Mellit, A., 2012. "Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study," Renewable Energy, Elsevier, vol. 48(C), pages 146-154.
    6. Cecilia Martinez-Castillo & Gonzalo Astray & Juan Carlos Mejuto, 2021. "Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models," Energies, MDPI, vol. 14(8), pages 1-16, April.
    7. Nespoli, Alfredo & Niccolai, Alessandro & Ogliari, Emanuele & Perego, Giovanni & Collino, Elena & Ronzio, Dario, 2022. "Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery," Applied Energy, Elsevier, vol. 305(C).
    8. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    9. Wang, Lunche & Kisi, Ozgur & Zounemat-Kermani, Mohammad & Salazar, Germán Ariel & Zhu, Zhongmin & Gong, Wei, 2016. "Solar radiation prediction using different techniques: model evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 384-397.
    10. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    11. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    12. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
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