RT info:eu-repo/semantics/article
T1 A soft computing method for detecting lifetime building thermal insulation failures
A1 Sedano, Javier
A1 Curiel Herrera, Leticia Elena
A1 Corchado, Emilio
A1 Cal, Enrique de la
A1 Villar, José Ramón
K1 Informática
K1 Computer science
AB The detection of thermal insulation failures in buildings in operation responds to the challenge of improving building energy efficiency. This multidisciplinary study presents a novel four-step soft computing knowledge identification model called IKBIS to perform thermal insulation failure detection. It proposes the use of Exploratory Projection Pursuit methods to study the relation between input and output variables and data dimensionality reduction. It also applies system identification theory and neural networks for modeling the thermal dynamics of the building. Finally, the novel model is used to predict dynamic thermal biases, and two real cases of study as part of its empirical validation.
PB IOS Press
SN 1069-2509
YR 2010
FD 2010-04
LK http://hdl.handle.net/10259/8583
UL http://hdl.handle.net/10259/8583
LA eng
NO This research has been partially supported through projects of the Juna of Castilla y León (JCyL): [BU006A08] and Business intelligent for production, Technological Institute of Castilla y León (ITCL) and investment and services agency (ADE), project of the Spanish Ministry of Education and Innovation [CIT-020000-2008-2] and project of Spanish Ministry of Science and Technology [TIN2008-06681-C06-04].
DS Repositorio Institucional de la Universidad de Burgos
RD 04-dic-2024