2026-04-28T05:02:07Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/85782024-02-06T01:05:23Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109col_10259_7409

Sedano, Javier 2bf97993-b410-4fe4-9d0b-5cd90272a1b5 600 Villar, José Ramón e18b6fc4-d919-45d6-ae7f-cf9259f6c918 600 Curiel Herrera, Leticia Elena 824 600 Cal, Enrique de la d09470f9-c7e2-45ab-8101-c1c1424546e5 600 Corchado, Emilio 125 600 0000-0001-8560-3991 2024-02-05T11:13:07Z 2024-02-05T11:13:07Z 2010 0302-9743 http://hdl.handle.net/10259/8578 10.1007/978-3-642-13033-5_65 1611-3349 Trabajo presentado en: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2010 Improving the detection of thermal insulation failures in buildings includes the development of models for heating process and fabric gain -heat flux through exterior walls in the building-. Thermal insulation standards are now contractual obligations in new buildings, the energy efficiency in the case of buildings constructed before the regulations adopted is still an open issue, and the assumption is that it will be based on heat flux and conductivity measurement. A three-step procedure is proposed in this study that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different variables is specifically modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, a supervised neural model and identification techniques are applied, in order to detect the heat flux through exterior walls in the building. The reliability of the proposed method is validated for a winter zone, associated to several cities in Spain. application/pdf eng Springer Nature Lecture Notes in Computer Science. 2010, V. 6098, p. 636-645 https://doi.org/10.1007/978-3-642-13033-5_65 Computational Intelligence Soft computing Systems Identification Systems Artificial Neural Networks Non-linear Systems Informática Computer science Modelling of Heat Flux in Building Using Soft-Computing Techniques info:eu-repo/semantics/bookPart info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess 6098 636 645 THUMBNAIL Sedano-Modelling_heat_flux_building_2010.pdf.jpg Sedano-Modelling_heat_flux_building_2010.pdf.jpg IM Thumbnail image/jpeg 3997 https://riubu.ubu.es/bitstream/10259/8578/3/Sedano-Modelling_heat_flux_building_2010.pdf.jpg 36f91b79bbd3b3652f0f70614b5ee721 MD5 3 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/8578/2/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 2 ORIGINAL Sedano-Modelling_heat_flux_building_2010.pdf Sedano-Modelling_heat_flux_building_2010.pdf application/pdf 485426 https://riubu.ubu.es/bitstream/10259/8578/1/Sedano-Modelling_heat_flux_building_2010.pdf ed47c105e523725227af93156ddb1694 MD5 1 10259/8578 oai:riubu.ubu.es:10259/8578 2024-02-06 02:05:23.786 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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