Martínez-Castillo, C.; Astray, G.; Mejuto, J.C. Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models. Energies2021, 14, 2332.
Martínez-Castillo, C.; Astray, G.; Mejuto, J.C. Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models. Energies 2021, 14, 2332.
Martínez-Castillo, C.; Astray, G.; Mejuto, J.C. Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models. Energies2021, 14, 2332.
Martínez-Castillo, C.; Astray, G.; Mejuto, J.C. Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models. Energies 2021, 14, 2332.
Abstract
Different machine learning models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to predict 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 machine model was checked in two independent stations. The results obtained confirmed that the best ML methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 122.6·10kJ/(m2∙day) and 113.6·10kJ/(m2∙day), respectively, and predict conveniently for independent stations, 201.3·10kJ/(m2∙day) and 209.4·10kJ/(m2∙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; machine learning; artificial neural network; random forest; vector support machine
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
