2026-04-29T20:42:05Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/85802024-02-06T01:05:22Zcom_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 2024-02-05T11:33:21Z 2024-02-05T11:33:21Z 2009 0302-9743 http://hdl.handle.net/10259/8580 10.1007/978-3-642-04394-9_95 1611-3349 Trabajo presentado en: International Conference on Intelligent Data Engineering and Automated Learning, 2009 Improving the detection of thermal insulation in buildings –which includes the development of models for heating and ventilation processes and fabric gain - could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon footprints of domestic heating systems. Thermal insulation standards are now contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those standards. Lighting, occupancy, set point temperature profiles, air conditioning and ventilation services all increase the complexity of measuring insulation efficiency. The identification of thermal insulation failure can help to reduce energy consumption in heating systems. Conventional methods can be greatly improved through the application of hybridized machine learning techniques to detect thermal insulation failures when a building is in operation. A three-step procedure is proposed in this paper 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 modelled, for each building type and climate zone. Secondly, Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, neural projections and identification techniques are applied, in order to detect fluctuations in room temperatures and, in consequence, thermal insulation failures. The reliability of the proposed method is validated in three winter zone C cities in Spain. Although a great deal of further research remains to be done in this field, the proposed system is expected to outperform conventional methods described in Spanish building codes that are used to calculate energetic profiles in domestic and residential buildings. application/pdf eng Springer Nature Lecture Notes in Computer Science. 2009, V. 5788, p. 773-782 https://doi.org/10.1007/978-3-642-04394-9_95 Feature selection Heating System Machine Intelligence Improve Energy Efficiency Indoor Temperature Informática Computer science Improving Energy Efficiency in Buildings Using Machine Intelligence info:eu-repo/semantics/bookPart info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess 5788 773 782 THUMBNAIL Sedano-Improving_efficiency_buildings_machine_intelligence_2009.pdf.jpg Sedano-Improving_efficiency_buildings_machine_intelligence_2009.pdf.jpg IM Thumbnail image/jpeg 4120 https://riubu.ubu.es/bitstream/10259/8580/3/Sedano-Improving_efficiency_buildings_machine_intelligence_2009.pdf.jpg cbae293df5beddb6a87a994740c81173 MD5 3 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/8580/2/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 2 ORIGINAL Sedano-Improving_efficiency_buildings_machine_intelligence_2009.pdf Sedano-Improving_efficiency_buildings_machine_intelligence_2009.pdf application/pdf 196915 https://riubu.ubu.es/bitstream/10259/8580/1/Sedano-Improving_efficiency_buildings_machine_intelligence_2009.pdf 66e0027735866fb1d653ab1ac742f81d MD5 1 10259/8580 oai:riubu.ubu.es:10259/8580 2024-02-06 02:05:22.705 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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