RT Journal Article
T1 Modelling and prediction of monthly global irradiation using different prediction models
A1 Martinez Castillo, Cecilia
A1 Astray Dopazo, Gonzalo
A1 Mejuto Fernández, Juan Carlos
K1 1203.04 Inteligencia Artificial
K1 2106.01 Energía Solar
K1 2502 Climatología
AB 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.
PB Energies
SN 19961073
YR 2021
FD 2021-04-20
LK http://hdl.handle.net/11093/2136
UL http://hdl.handle.net/11093/2136
LA eng
NO Energies, 14(8): 2332 (2021)
NO Universidade de Vigo
DS Investigo
RD 14-sep-2024