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oai:riubu.ubu.es:10259/74142023-03-27T13:07:33Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Sedano, Javier Corchado, Emilio Curiel Herrera, Leticia Elena Villar, José Ramón Cal, Enrique de la 2023-02-07T13:11:27Z 2023-02-07T13:11:27Z 2010 1210-0552 http://hdl.handle.net/10259/7414 2336-4335 Trabajo publicado en: Special Issue on 5th International Conference on Hybrid Artificial Intelligence Systems The 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. This research has been partially supported through projects of the Junta of Castilla and León (JCyL): [BU006A08], TIN2010-21272-C02-01 from the Spanish Ministry of Science and Innovation, the project of Spanish Ministry and Innovation [PID 560300-2009-11], the project of the Spanish Ministry of Education and Innovation [CIT-020000-2008-2] and [CIT-020000-2009-12], the project of Spanish Ministry of Science and Technology [TIN2008-06681-C06-04] and Grupo Antolin Ingenieria, S.A., within the framework of project MAGNO2008 - 1028.- CENIT also funded by the same Government Ministry. application/pdf eng Academy of Sciences of the Czech Republic. Institute of Computer Science Neural Network World. 2010, V. 20, n. 7, p. 883 - 898 http://www.nnw.cz/obsahy10.html info:eu-repo/grantAgreement/Junta de Castilla y León//BU006A08//Arquitectura distribuida inteligente para la gestión de entornos de dependencia y hospitalarios/ info:eu-repo/grantAgreement/MICINN//TIN2010-21272-C02-01/ES/MODELO INTELIGENTE DE ANALISIS Y SIMULACION DE PROCESOS INDUSTRIALES: TOMA DE DECISIONES Y CONTROL DE PROCESOS/ info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2008-2011/PID-560300-2009-11/ES/La excelencia española en rapid manufacturing, desarrollo y aplicación de la tecnología de deformación incremental de segunda generación para aplicaciones estratégicas/ISF_2G/ info:eu-repo/grantAgreement/MICINN//CIT-020000-2008-2/ES/Desarrollo de un sistema experto para el ajuste automático de fresadoras (QUICK)/ info:eu-repo/grantAgreement/MICINN//CIT-020000-2009-12/ES/Desarrollo de un Sistema Inteligente Híbrido para optimizar la interacción máquina-proceso en los procesos productivos orientados al sector eólico y de transporte (FOEHN)/ info:eu-repo/grantAgreement/MICINN//TIN2008-06681-C06-04/ES/KEEL-CTNC: MODELOS DE APRENDIZAJE EVOLUTIVO BASADOS EN DATOS DE BAJA CALIDAD Y SISTEMAS GENETICO-BORROSOS. CONJUNTOS DE DATOS DISTRIBUIDOS PARA PROBLEMAS DE ALTA DIMENSIONALIDAD/ info:eu-repo/grantAgreement/MICINN//MAGNO2008-1028-CENIT/ Computational intelligence Soft computing Identification Systems Artificial Neural Networks Non-linear Systems Energetic Efficiency Informática Computer science Detection of heat flux failures in building using a soft computing diagnostic system info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess