RT info:eu-repo/semantics/article
T1 Detection of heat flux failures in building using a soft computing diagnostic system
A1 Sedano, Javier
A1 Corchado, Emilio
A1 Curiel Herrera, Leticia Elena
A1 Villar, José Ramón
A1 Cal, Enrique de la
K1 Computational intelligence
K1 Soft computing
K1 Identification Systems
K1 Artificial Neural Networks
K1 Non-linear Systems
K1 Energetic Efficiency
K1 Informática
K1 Computer science
AB 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.
PB Academy of Sciences of the Czech Republic. Institute of Computer Science
SN 1210-0552
YR 2010
FD 2010
LK http://hdl.handle.net/10259/7414
UL http://hdl.handle.net/10259/7414
LA eng
NO Trabajo publicado en: Special Issue on 5th International Conference on Hybrid Artificial Intelligence Systems
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 04-may-2024