1. Modelling and prediction of monthly global irradiation using different prediction models
- Author
-
Juan C. Mejuto, Gonzalo Astray, and Cecilia Martinez-Castillo
- Subjects
Technology ,Control and Optimization ,010504 meteorology & atmospheric sciences ,Mean squared error ,020209 energy ,2106.01 Energía Solar ,Energy Engineering and Power Technology ,02 engineering and technology ,vector support machine ,01 natural sciences ,Latitude ,Altitude ,Linear regression ,Statistics ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Engineering (miscellaneous) ,0105 earth and related environmental sciences ,Mathematics ,Artificial neural network ,Renewable Energy, Sustainability and the Environment ,prediction ,solar irradiation ,2502 Climatología ,Random forest ,1203.04 Inteligencia Artificial ,Longitude ,Predictive modelling ,artificial neural network ,random forest ,Energy (miscellaneous) - 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/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(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. Universidade de Vigo Xunta de Galicia | Ref. POS-B / 2016/001 Xunta de Galicia | Ref. K645 P.P.0000 421S 140.08
- Published
- 2021