Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/55588
Campo DC Valoridioma
dc.contributor.authorMazorra Aguiar, Luisen_US
dc.contributor.authorLauret, P.en_US
dc.contributor.authorDíaz Reyes, Felipeen_US
dc.contributor.authorOrtegón, A.en_US
dc.contributor.authorPérez-Suárez, R.en_US
dc.date.accessioned2019-05-31T12:55:04Z-
dc.date.available2019-05-31T12:55:04Z-
dc.date.issued2016en_US
dc.identifier.issn2172-038Xen_US
dc.identifier.urihttp://hdl.handle.net/10553/55588-
dc.description.abstractForecasting of global solar radiation is an important tool for power systems planning and operation, especially in island grids. The aim of this paper is the analysis of an artificial neural network as a reliable method to obtain a daily forecast for solar radiation. Some different tests are proposed to obtain the optimal ANN that will capture the underlying physical process that generates the data. In the present study, the available data come from seven measuring stations throughout the Gran Canaria Island along six years. ANN was trained and tested only with past ground measurement solar radiation and other meteorological data available at measurement stations as inputs.en_US
dc.languageengen_US
dc.relation.ispartofRenewable energy and power quality journalen_US
dc.sourceRenewable energy and power quality journal [ISSN 2172-038X], v. 1 (14), p. 992-996en_US
dc.subject33 Ciencias tecnológicasen_US
dc.subject.otherArtificial Neural Networksen_US
dc.subject.otherForecastingen_US
dc.subject.otherPerceptronen_US
dc.subject.otherSolar radiationen_US
dc.titleDaily global solar radiation estimation for Gran Canaria Island using artificial neural networksen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticlees
dc.identifier.doi10.24084/repqj14.546
dc.identifier.scopus85072163029
dc.contributor.authorscopusid57211001651
dc.contributor.authorscopusid7004327525
dc.contributor.authorscopusid26429057600
dc.contributor.authorscopusid57210998489
dc.contributor.authorscopusid57210998256
dc.description.lastpage996-
dc.identifier.issue14-
dc.description.firstpage992-
dc.relation.volume1-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.identifier.ulpgces
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR SIANI: Modelización y Simulación Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Ingeniería Eléctrica-
crisitem.author.orcid0000-0002-9746-7461-
crisitem.author.orcid0000-0001-7874-6636-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameMazorra Aguiar, Luis-
crisitem.author.fullNameDíaz Reyes, Felipe-
Colección:Artículos
miniatura
pdf
Adobe PDF (590,19 kB)
Vista resumida

Visitas

124
actualizado el 01-nov-2024

Descargas

145
actualizado el 01-nov-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.