2026-09-09T19:24:40Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/85832024-02-06T01:05:26Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Repositorio Institucional de la Universidad de Burgos
author
Sedano, Javier
author
Curiel Herrera, Leticia Elena
author
Corchado, Emilio
author
Cal, Enrique de la
author
Villar, José Ramón
2024-02-05T12:26:36Z
2024-02-05T12:26:36Z
2010-04
1069-2509
http://hdl.handle.net/10259/8583
10.3233/ICA-2010-0337
1875-8835
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.
eng
A soft computing method for detecting lifetime building thermal insulation failures
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/8583/1/Sedano-icae_2010.pdf
File
MD5
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application/pdf
Sedano-icae_2010.pdf