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dc.contributor.authorDieste Velasco, Mª Isabel 
dc.contributor.authorGarcía Rodríguez, Sol 
dc.contributor.authorGarcía Rodríguez, Ana 
dc.contributor.authorDiez Mediavilla, Montserrat 
dc.contributor.authorAlonso Tristán, Cristina 
dc.date.accessioned2023-03-13T12:15:46Z
dc.date.available2023-03-13T12:15:46Z
dc.date.issued2023-01
dc.identifier.urihttp://hdl.handle.net/10259/7537
dc.description.abstractIn the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (IUV), on the basis of data collected at Burgos (Spain) during an experimental campaign between March 2020 and May 2022. The aim is to explore the effectiveness of a range of variables for modelling horizontal ultraviolet irradiance through a comparison of supervised artificial neural network (ANN) and regression model results. A preliminary feature selection process using the Pearson correlation coefficient was sufficient to determine 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), sky clearness (ε), cloud cover (Cc), horizontal diffuse irradiance (IDH), and sky brightness (∆). The ANN models yielded results of greater accuracy than the regression models.en
dc.description.sponsorshipThis 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.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIen
dc.relation.ispartofApplied Sciences. 2023, V. 13, n. 3, 1473en
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectUV irradianceen
dc.subjectANNen
dc.subjectModelingen
dc.subjectMultilinear regression modelsen
dc.subject.otherElectrotecniaes
dc.subject.otherElectrical engineeringen
dc.subject.otherMatemáticases
dc.subject.otherMathematicsen
dc.titleModeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Modelsen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/app13031473es
dc.identifier.doi10.3390/app13031473
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098900-B-I00/ES/ANALISIS ESPECTRAL DE LA RADIACION SOLAR: APLICACIONES CLIMATICAS, ENERGETICAS Y BIOLOGICAS/es
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/TED2021-131563B-I00/ES/Modelado espectral de la radiación solar en entornos urbanos: una oportunidad para la sostenibilidad de las ciudades/es
dc.relation.projectIDinfo:eu-repo/grantAgreement/Junta de Castilla y León//INVESTUN%2F19%2FBU%2F0004//Valoración técnica de los niveles de exposición a radiación solar en trabajos de exterior: identificación de grupos de riesgo y medidas de prevención/es
dc.identifier.essn2076-3417
dc.journal.titleApplied Sciencesen
dc.volume.number13es
dc.issue.number3es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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