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dc.contributor.authorQuintián Pardo, Héctor
dc.contributor.authorCalvo Rolle, José L.
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.date.accessioned2017-09-05T10:59:37Z
dc.date.available2017-09-05T10:59:37Z
dc.date.issued2014
dc.identifier.citationInformática. Volumen 25 (2), pp. 265-282.
dc.identifier.issn0868-4952
dc.identifier.urihttp://hdl.handle.net/10366/134316
dc.description.abstractThe aim of this study is to predict the energy generated by a solar thermal system. To achieve this, a hybrid intelligent system was developed based on local regression models with low complexity and high accuracy. Input data is divided into clusters by using a Self Organization Maps; a local model will then be created for each cluster. Different regression techniques were tested and the best one was chosen. The novel hybrid regression system based on local models is empirically verified with a real dataset obtained by the solar thermal system of a bioclimatic house.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleA Hybrid Regression System Based on Local Models for Solar Energy Prediction
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
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