2024-03-29T13:37:06Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1344022024-03-13T09:52:59Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243
Learning and training techniques in fuzzy control for energy efficiency in buildings
Sedano Franco, Javier
Villar Flecha, José R.
Curiel, Leticia
Corchado RodrÃguez, Emilio Santiago
de la Cal, Enrique
Computer Science
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.
2017-09-05T11:01:54Z
2017-09-05T11:01:54Z
2011
info:eu-repo/semantics/article
Logic Journal of IGPL. Volumen 20 (4), pp. 757-769. Oxford University Press (OUP).
1367-0751 (Print) / 1368-9894 (Online)
http://hdl.handle.net/10366/134402
en
Attribution-NonCommercial-NoDerivs 3.0 Unported
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
application/pdf
Oxford University Press (OUP)