RT info:eu-repo/semantics/article
T1 Learning and training techniques in fuzzy control for energy efficiency in buildings
A1 Sedano, Javier
A1 Villar, José Ramón
A1 Curiel Herrera, Leticia Elena
A1 Corchado, Emilio
A1 Cal, Enrique de la
K1 Computational Intelligence
K1 Soft computing Systems
K1 Mathematical and Identification Systems
K1 Artificial Neural Networks
K1 Non-linear Systems
K1 Informática
K1 Computer science
K1 Matemáticas
K1 Mathematics
AB 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.
PB Oxford University Press
SN 1367-0751
YR 2011
FD 2011-02
LK http://hdl.handle.net/10259/8582
UL http://hdl.handle.net/10259/8582
LA spa
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 09-may-2024