2026-06-02T03:27:35Zhttps://riubu.ubu.es/oai/request

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

00925njm 22002777a 4500 dc Sedano, Javier author Villar, José Ramón author Curiel Herrera, Leticia Elena author Corchado, Emilio author Cal, Enrique de la author 2011-02 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. 1367-0751 http://hdl.handle.net/10259/8582 10.1093/jigpal/jzr022 1368-9894 Computational Intelligence Soft computing Systems Mathematical and Identification Systems Artificial Neural Networks Non-linear Systems Learning and training techniques in fuzzy control for energy efficiency in buildings