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dc.contributor.authorDíaz–Vico, Davides_ES
dc.contributor.authorTorres–Barrán, Albertoes_ES
dc.contributor.authorOmari, Adiles_ES
dc.contributor.authorDorronsoro Ibero, José Ramón es_ES
dc.contributor.otherUAM. Departamento de Ingeniería Informáticaes_ES
dc.date.accessioned2022-06-28T15:35:44Zen_US
dc.date.available2022-06-28T15:35:44Zen_US
dc.date.issued2017-12-01en_US
dc.identifier.citationDíaz–Vico, D., Torres–Barrán, A., Omari, A., Dorronsoro, José R., Deep Neural Networks for Wind and Solar Energy Prediction. Neural Processing Letters 46 (2017): 829-844en_US
dc.identifier.issn1573-773X (online)en_US
dc.identifier.issn1370-4621 (print)en_US
dc.identifier.urihttp://hdl.handle.net/10486/702830en_US
dc.descriptionThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11063-017-9613-7en_US
dc.description.abstractDeep Learning models are recently receiving a large attention because of their very powerful modeling abilities, particularly on inputs that have a intrinsic one- or two-dimensional structure that can be captured and exploited by convolutional layers. In this work we will apply Deep Neural Networks (DNNs) in two problems, wind energy and daily solar radiation prediction, whose inputs, derived from Numerical Weather Prediction systems, have a clear spatial structure. As we shall see, the predictions of single deep models and, more so, of DNN ensembles can improve on those of Support Vector Regression, a Machine Learning method that can be considered the state of the art for regressionen_US
dc.description.sponsorshipWith partial support from Spain’s Grants TIN2013-42351-P (MINECO), S2013/ICE2845 CASI-CAM-CM (Comunidad de Madrid), FACIL (Ayudas Fundación BBVA a Equipos de Investigación Científica 2016) and the UAM–ADIC Chair for Data Science and Machine Learning. The second author is also kindly supported by the FPU-MEC Grant AP-2012-5163. The authors gratefully acknowledge access to the MARS repository granted by the ECMWF, the use of the facilities of Centro de Computación Científica (CCC) at UAM and thank Red Eléctrica de España for kindly supplying wind energy production data and to Sotavento for making their production data publicly available.en_US
dc.format.extent17 pag.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofNeural Processing Lettersen_US
dc.rights© Springer Natureen_US
dc.subject.otherConvolutional neural networken_US
dc.subject.otherDeep learningen_US
dc.subject.otherSolar energyen_US
dc.subject.otherWind energyen_US
dc.titleDeep Neural Networks for Wind and Solar Energy Predictionen_US
dc.typearticleen_US
dc.subject.ecienciaInformáticaes_ES
dc.date.embargoend2018-12-01en_US
dc.relation.publisherversionhttps://doi.org/10.1007/s11063-017-9613-7en_US
dc.identifier.doi10.1007/s11063-017-9613-7en_US
dc.identifier.publicationfirstpage829es_ES
dc.identifier.publicationissue3es_ES
dc.identifier.publicationlastpage844es_ES
dc.identifier.publicationvolume46es_ES
dc.relation.projectIDGobierno de España. TIN2013-42351-Pes_ES
dc.relation.projectIDComunidad de Madrid. S2013/ICE2845/CASI-CAM-CMes_ES
dc.type.versioninfo:eu-repo/semantics/updatedVersionen_US
dc.rights.ccNo Aplicaes_ES
dc.rights.accessRightsembargoedAccessen_US
dc.facultadUAMEscuela Politécnica Superiores_ES


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