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dc.contributor.authorSedano Franco, Javier
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorCuriel, Leticia
dc.contributor.authorVillar Flecha, José R.
dc.contributor.authorde la Cal, Enrique
dc.date.accessioned2017-09-05T11:01:56Z
dc.date.available2017-09-05T11:01:56Z
dc.date.issued2010
dc.identifier.citationNeural Network World. Volumen 20 (6), pp. 883-898. Institute of Computer Science AS CR.
dc.identifier.issn1210-0552
dc.identifier.urihttp://hdl.handle.net/10651/36107
dc.identifier.urihttp://hdl.handle.net/10366/134406
dc.description.abstractThe detection of insulation failures in buildings could potentially conserve energy supplies and improve future designs. Improvements to thermal insulation in buildings include the development of models to assess fabric gain - heat flux through exterior walls in the building- and heating processes. Thermal insulation standards are now contractual obligations in new buildings, and the energy efficiency of buildings constructed prior to these regulations has yet to be determined. The main assumption is that it will be based on heat flux and conductivity measurement. Diagnostic systems to detect thermal insulation failures should recognize anomalous situations in a building that relate to insulation, heating and ventilation. This highly relevant issue in the construction sector today is approached through a novel intelligent procedure that can be programmed according to local building and heating system regulations and the specific features of a given climate zone. It is based on the following phases. Firstly, the dynamic thermal performance of different variables is specifically modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning extracts the relevant features. Finally, a supervised neural model and identification techniques constitute the model for the diagnosis of thermal insulation failures in building due to the heat flux through exterior walls, using relevant features of the data set. The reliability of the proposed method is validated with real data sets from several Spanish cities in winter time.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherInstitute of Computer Science AS CR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleDetection of heat flux failures in building using a soft computing diagnostic system
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported