Ultraviolet erythemal irradiance (UVER) under different sky conditions in Burgos, Spain: multilinear regression and artificial neural network models
Fecha
2023Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
Impacto
|
10.3390/app131910979
Resumen
Different strategies for modeling Global Horizontal UltraViolet Erythemal irradiance
(GHUVE) 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 (MLR)
and artificial neural n ...
[++]
Different strategies for modeling Global Horizontal UltraViolet Erythemal irradiance
(GHUVE) 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 (MLR)
and artificial neural network (ANN) techniques were employed to model GHUVE under different
sky conditions (all skies, overcast, intermediate, and clear skies), classified according to the CIE
standard on a 10 min basis. ANN models of GHUVE outperform those based on MLR according
to the traditional statistical indices used in this study (R2, MBE, and nRMSE). Moreover, the work
proposes a simple all-sky ANN model of GHUVE based on usually recorded variables at ground
meteorological stations. [--]
Materias
Ultraviolet erythemal irradiance,
UVER,
Statistical analysis,
Modeling,
ANN,
Multilinear regression models
Editor
MDPI
Publicado en
Applied Sciences 2023, 13, 10979
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC
Versión del editor
Entidades Financiadoras
This research was funded by MCIN/AEI/ 10.13039/501100011033 and the “European
Union Next Generation EU/PRTR grant numbers TED2021-131563B-I00 and PID2022-139477OB-I00
and Junta de Castilla y León, grant number INVESTUN/19/BU/0004.