Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Cecilia Martinez-Castillo, Gonzalo Astray and Juan Carlos Mejuto
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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
Juan Carlos Mejuto: Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain

Energies, 2021, vol. 14, issue 8, 1-16

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.

Keywords: prediction; solar irradiation; artificial neural network; random forest; vector support machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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