2026-05-01T18:33:36Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/110662025-11-18T10:42:00Zcom_10259_4393com_10259_5086com_10259_2604col_10259_4394

Ultraviolet Erythemal Irradiance (UVER) under Different Sky Conditions in Burgos, Spain: Multilinear Regression and Artificial Neural Network Models García Rodríguez, Sol García Rodríguez, Ana Granados López, Diego García, Ignacio Alonso Tristán, Cristina Ultraviolet erythemal irradiance UVER Statistical analysis Modeling ANN Multilinear regression models Redes neuronales artificiales Neural networks (Computer science) Different strategies for modeling Global Horizontal UltraViolet Erythemal irradiance (G⁡H⁡U⁢V⁢E) based on meteorological parameters measured in Burgos (Spain) have been developed. The experimental campaign ran from September 2020 to June 2022. The selection of relevant variables for modeling was based on Pearson’s correlation coefficient. Multilinear Regression Model (M⁢L⁢R) and artificial neural network (A⁢N⁢N) techniques were employed to model G⁡H⁡U⁢V⁢E under different sky conditions (all skies, overcast, intermediate, and clear skies), classified according to the C⁢I⁢E standard on a 10 min basis. A⁢N⁢N models of G⁡H⁡U⁢V⁢E outperform those based on MLR according to the traditional statistical indices used in this study (R2, M⁢B⁢E, and n⁢R⁢M⁢S⁢E). Moreover, the work proposes a simple all-sky A⁢N⁢N model of G⁡H⁡U⁢V⁢E based on usually recorded variables at ground meteorological stations. 2025-11-17T08:08:34Z 2025-11-17T08:08:34Z 2023-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://hdl.handle.net/10259/11066 10.3390/app131910979 2076-3417 eng Applied Sciences. 2023, V. 13, n. 19, 10979 https://doi.org/10.3390/app131910979 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf MDPI