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dc.contributor.author | García Rodríguez, Ana | |
dc.contributor.author | García Rodríguez, Sol | |
dc.contributor.author | Granados López, Diego | |
dc.contributor.author | Diez Mediavilla, Montserrat | |
dc.contributor.author | Alonso Tristán, Cristina | |
dc.date.accessioned | 2023-01-13T11:25:47Z | |
dc.date.available | 2023-01-13T11:25:47Z | |
dc.date.issued | 2022-03 | |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | http://hdl.handle.net/10259/7237 | |
dc.description.abstract | Four models for predicting Photosynthetically Active Radiation (PAR) were obtained through MultiLinear Regression (MLR) and an Artificial Neural Network (ANN) based on 10 meteorological indices previously selected from a feature selection algorithm. One model was developed for all sky conditions and the other three for clear, partial, and overcast skies, using a sky classification based on the clearness index (kt). The experimental data were recorded in Burgos (Spain) at ten-minute intervals over 23 months between 2019 and 2021. Fits above 0.97 and Root Mean Square Error (RMSE) values below 7.5% were observed. The models developed for clear and overcast sky conditions yielded better results. Application of the models to the seven experimental ground stations that constitute the Surface Radiation Budget Network (SURFRAD) located in different Köppen climatic zones of the USA yielded fitted values higher than 0.98 and RMSE values less than 11% in all cases regardless of the sky type. | en |
dc.description.sponsorship | This research was funded by the Spanish Ministry of Science and Innovation, grant number RTI2018-098900-B-I00, and Consejería de Empleo e Industria, Junta de Castilla y León, grant number INVESTUN/19/BU-0004. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | es |
dc.publisher | MDPI | en |
dc.relation.ispartof | Applied Sciences. 2022, V. 12, n. 5, 2372 | en |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Photosynthetically active radiation | en |
dc.subject | kt sky classification | en |
dc.subject | ANN | en |
dc.subject | Multilinear regression models | en |
dc.subject.other | Electrotecnia | es |
dc.subject.other | Electrical engineering | en |
dc.title | Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones | en |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.relation.publisherversion | https://doi.org/10.3390/app12052372 | es |
dc.identifier.doi | 10.3390/app12052372 | |
dc.relation.projectID | info: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.projectID | info: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.essn | 2076-3417 | |
dc.journal.title | Applied Sciences | en |
dc.volume.number | 12 | es |
dc.issue.number | 5 | es |
dc.page.initial | 2372 | es |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |