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Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Author

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  • Cecilia Martinez-Castillo

    (Department of Analytical and Food Chemistry, Nutrition and Bromatology, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain)

  • Gonzalo Astray

    (Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain
    CITACA, University of Vigo, Campus Auga, 32004 Ourense, Spain)

  • Juan Carlos Mejuto

    (Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain)

Abstract

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation ( MGI ) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m 2 ∙day) and 1136 kJ/(m 2 ∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m 2 ∙day) and 2094 kJ/(m 2 ∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2332-:d:539808
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Tomasz Chrulski & Mariusz Łaciak, 2021. "Analysis of Natural Gas Consumption Interdependence for Polish Industrial Consumers on the Basis of an Econometric Model," Energies, MDPI, vol. 14(22), pages 1-26, November.

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