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dc.contributor.authorBasurto Hornillos, Nuño 
dc.contributor.authorArroyo Puente, Ángel 
dc.contributor.authorVega, Rafael
dc.contributor.authorQuintián, Héctor
dc.contributor.authorCalvo-Rolle, José Luis
dc.contributor.authorHerrero Cosío, Álvaro 
dc.date.accessioned2024-01-08T12:45:26Z
dc.date.available2024-01-08T12:45:26Z
dc.date.issued2019-09
dc.identifier.issn0045-7906
dc.identifier.urihttp://hdl.handle.net/10259/8246
dc.description.abstractThere is wide acknowledgement that solar energy is a promising and renewable source of electricity. However, complementary sources are sometimes required, due to its limited capacity, in order to satisfy user demand. A Hybrid Intelligent System (HIS) is proposed in this paper to optimize the range of possible solar energy and power grid combinations. It is designed to predict the energy generated by any given solar thermal system. To do so, the novel HIS is based on local models that implement both supervised learning (artificial neural networks) and unsupervised learning (clustering). These techniques are combined and applied to a realworld installation located in Spain. Alternative models are compared and validated in this case study with data from a whole year. With an optimum parameter fit, the proposed system managed to calculate the solar energyproduced by the panel with an error that was lower than 10-4 in 86% of cases.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofComputers & Electrical Engineering. 2019, V. 78, p. 373-387es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectHybrid Intelligent Systemen
dc.subjectClusteringen
dc.subjectRegressionen
dc.subjectNeural networksen
dc.subjectSolar Energyen
dc.subjectRenewable energiesen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleA Hybrid Intelligent System to forecast solar energy productionen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1016/j.compeleceng.2019.07.023es
dc.identifier.doi10.1016/j.compeleceng.2019.07.023
dc.journal.titleComputers & Electrical Engineeringen
dc.volume.number78es
dc.page.initial373es
dc.page.final387es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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