2026-05-07T19:30:19Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/85822024-02-06T01:05:28Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Sedano, Javier 2bf97993-b410-4fe4-9d0b-5cd90272a1b5 600 Villar, José Ramón e18b6fc4-d919-45d6-ae7f-cf9259f6c918 600 Curiel Herrera, Leticia Elena 824 600 Corchado, Emilio 125 600 0000-0001-8560-3991 Cal, Enrique de la d09470f9-c7e2-45ab-8101-c1c1424546e5 600 2024-02-05T12:00:19Z 2024-02-05T12:00:19Z 2011-02 1367-0751 http://hdl.handle.net/10259/8582 10.1093/jigpal/jzr022 1368-9894 A novel procedure for learning Fuzzy Controllers (FC) is proposed that concerns with energy efficiency issues in distributing electrical energy to heaters in an electrical energy heating system. Energy rationalization together with temperature control can significantly improve energy efficiency, by efficiently controlling electrical heating systems and electrical energy consumption. The novel procedure, which improves the training process, is designed to train the FC, as well as to run the control algorithm and to carry out energy distribution. Firstly, the dynamic thermal performance of different variables is mathematically modelled for each specific building type and climate zone. Secondly, an exploratory projection pursuit method is used to extract the relevant features. Finally, a supervised dynamic neural network model and identification techniques are applied to FC learning and training. The FC rule-set and parameter-set learning process is a multi-objective problem that minimizes both the indoor temperature error and the energy deficit in the house. The reliability of the proposed procedure is validated for a city in a winter zone in Spain. This work has been partially supported through the Junta of Castilla and León (JCyL): [BU006A08], the Spanish Ministry of Science and Innovation [PID 560300-2009-11], the Spanish Ministry of Education and Innovation [CIT-020000-2008-2], the Spanish Ministry of Science and Technology [TIN2008-06681-C06-04] and Grupo Antolin Ingenieria, S.A., within the framework of MAGNO2008 - 1028.- CENIT also funded by the same Government Ministry. We would like to extend our thanks to Phd. Magnus Nørgaard for his freeware version of Matlab Neural Network Based System Identification Toolbox. application/pdf spa Oxford University Press Logic Journal of the IGPL. 2012, V. 20, n. 4, p. 757-769 https://doi.org/10.1093/jigpal/jzr022 Computational Intelligence Soft computing Systems Mathematical and Identification Systems Artificial Neural Networks Non-linear Systems Informática Matemáticas Computer science Mathematics Learning and training techniques in fuzzy control for energy efficiency in buildings info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess Logic Journal of the IGPL 20 4 757 769 THUMBNAIL Sedano-ljigpl_2011.pdf.jpg Sedano-ljigpl_2011.pdf.jpg IM Thumbnail image/jpeg 3728 https://riubu.ubu.es/bitstream/10259/8582/3/Sedano-ljigpl_2011.pdf.jpg 0889a8f830a0e0e81c1047db2354f963 MD5 3 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/8582/2/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 2 ORIGINAL Sedano-ljigpl_2011.pdf Sedano-ljigpl_2011.pdf application/pdf 209909 https://riubu.ubu.es/bitstream/10259/8582/1/Sedano-ljigpl_2011.pdf 3e67421d82765920ef868891745c9adf MD5 1 10259/8582 oai:riubu.ubu.es:10259/8582 2024-02-06 02:05:28.125 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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