2026-05-07T18:20:01Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/74142023-03-27T13:07:33Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Detection of heat flux failures in building using a soft computing diagnostic system
Sedano, Javier
Corchado, Emilio
Curiel Herrera, Leticia Elena
Villar, José Ramón
Cal, Enrique de la
Computational intelligence
Soft computing
Identification Systems
Artificial Neural Networks
Non-linear Systems
Energetic Efficiency
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.
2023-02-07T13:11:27Z
2023-02-07T13:11:27Z
2023-02-07T13:11:27Z
2010
info:eu-repo/semantics/article
1210-0552
http://hdl.handle.net/10259/7414
2336-4335
eng
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/
info:eu-repo/semantics/openAccess
Academy of Sciences of the Czech Republic. Institute of Computer Science