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dc.contributor.authorMora-Lopez, Llanos 
dc.contributor.authorSidrach-de-Cardona-Ortin, Mariano 
dc.date.accessioned2019-11-13T12:30:23Z
dc.date.available2019-11-13T12:30:23Z
dc.date.created2019
dc.date.issued2019-11-13
dc.identifier.urihttps://hdl.handle.net/10630/18770
dc.description.abstractThe results obtained for the modeling and optimization of photovoltaic self-consumption facilities are presented. The study has been carried out for three Spanish cities with different climatic conditions. The self-consumption and self-sufficiency curves for different hourly consumption profiles have been obtained based on the installed peak power and the size of the battery. Different models of machine learning are proposed to predict these parameters. The input variables of these models are related to the configuration of the installation, its location and the type of consumption profile. The model with best predictions of self-sufficiency is Random Forest, which in cross-validation has a relative error of 5%. For the prediction of self-consumption, the model that performs best is the multilayer perceptron, with an average absolute error of 0.55 and an absolute relative error of 3%.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEnergía fotovoltaicaen_US
dc.subject.otherSelf-consumptionen_US
dc.subject.otherData mining modelsen_US
dc.subject.otherPhotovoltaic systemsen_US
dc.titleModels for the Optimization and Evaluation of Photovoltaic Self-Consumption Facilitiesen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitleInternational Solar World Congress 2019en_US
dc.relation.eventplaceSantiago, Chileen_US
dc.relation.eventdate11/2019en_US
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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