2026-06-02T03:27:12Zhttps://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 Villar, José Ramón Curiel Herrera, Leticia Elena Corchado, Emilio Cal, Enrique de la 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