dc.creator | Ferrero Bermejo, Jesús | es |
dc.creator | Gómez Fernández, Juan Francisco | es |
dc.creator | Olivencia Polo, Fernando | es |
dc.creator | Crespo Márquez, Adolfo | es |
dc.date.accessioned | 2019-08-30T18:19:50Z | |
dc.date.available | 2019-08-30T18:19:50Z | |
dc.date.issued | 2019-05 | |
dc.identifier.citation | Ferrero Bermejo, J., Gómez Fernández, J.F., Olivencia Polo, F. y Crespo Márquez, A. (2019). A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources. Applied Sciences, 9 (9). Article number 1844. | |
dc.identifier.issn | 2076-3417 | es |
dc.identifier.uri | https://hdl.handle.net/11441/88822 | |
dc.description.abstract | The generation of energy from renewable sources is subjected to very dynamic changes
in environmental parameters and asset operating conditions. This is a very relevant issue to be
considered when developing reliability studies, modeling asset degradation and projecting renewable
energy production. To that end, Artificial Neural Network (ANN) models have proven to be a
very interesting tool, and there are many relevant and interesting contributions using ANN models,
with different purposes, but somehow related to real-time estimation of asset reliability and energy
generation. This document provides a precise review of the literature related to the use of ANN
when predicting behaviors in energy production for the referred renewable energy sources. Special
attention is paid to describe the scope of the different case studies, the specific approaches that were
used over time, and the main variables that were considered. Among all contributions, this paper
highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc
advanced maintenance policies. The purpose is to offer the readers an overall picture per energy
source, estimating the significance that this tool has achieved over the last years, and identifying the
potential of these techniques for future dependability analysis. | es |
dc.description.sponsorship | Unión Europea H2020-MSCA-RISE-2014 | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad DPI2015-70842-R | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Applied Sciences, 9 (9). Article number 1844. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Renewable energy | es |
dc.subject | Artificial neural network | es |
dc.subject | Artificial intelligence | es |
dc.subject | Survey | es |
dc.title | A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I | es |
dc.relation.projectID | H2020-MSCA-RISE-2014 | es |
dc.relation.projectID | DPI2015-70842-R | es |
dc.relation.publisherversion | https://doi.org/10.3390/app9091844 | es |
idus.format.extent | 20 p. | es |
dc.journaltitle | Applied Sciences | es |
dc.publication.volumen | 9 | es |
dc.publication.issue | 9 | es |
dc.publication.initialPage | Article number 1844 | es |