RT info:eu-repo/semantics/article
T1 Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models
A1 Dieste Velasco, Mª Isabel
A1 García Rodríguez, Sol
A1 García Rodríguez, Ana
A1 Diez Mediavilla, Montserrat
A1 Alonso Tristán, Cristina
K1 UV irradiance
K1 ANN
K1 Modeling
K1 Multilinear regression models
K1 Electrotecnia
K1 Electrical engineering
K1 Matemáticas
K1 Mathematics
AB In the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (IUV), on the basis of data collectedat Burgos (Spain) during an experimental campaign between March 2020 and May 2022. The aimis to explore the effectiveness of a range of variables for modelling horizontal ultraviolet irradiancethrough a comparison of supervised artificial neural network (ANN) and regression model results.A preliminary feature selection process using the Pearson correlation coefficient was sufficient todetermine the variables for use in the models. The following variables and their influence on horizontal ultraviolet irradiance were analyzed: horizontal global irradiance (IGH), clearness index (kt),solar altitude angle (α), horizontal beam irradiance (IBH), diffuse fraction (D), temperature (T), skyclearness (ε), cloud cover (Cc), horizontal diffuse irradiance (IDH), and sky brightness (∆). The ANNmodels yielded results of greater accuracy than the regression models.
PB MDPI
YR 2023
FD 2023-01
LK http://hdl.handle.net/10259/7537
UL http://hdl.handle.net/10259/7537
LA eng
NO This research is a result of the project RTI2018-098900-B-I00 financed by the Spanish Ministry of Science and Innovation, project TED2021-131563B-I00 financed by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR», and Junta de Castilla y León, under grant number INVESTUN/19/BU/0004.
DS Repositorio Institucional de la Universidad de Burgos
RD 23-nov-2024