Machine learning methods to forecast temperature in buildings
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Other documents of the author: Mateo, Fernando; Carrasco, Juan José; Sellami, Abderrahim; Millán Giraldo, Mónica; Domínguez, Manuel; Soria Olivas, Emilio
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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http://dx.doi.org/10.1016/j.eswa.2012.08.030 |
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Title
Machine learning methods to forecast temperature in buildingsAuthor (s)
Date
2013Publisher
ElsevierISSN
0957-4174Bibliographic citation
Expert Systems with Applications Volume 40, Issue 4, March 2013, Pages 1061–1068Type
info:eu-repo/semantics/articlePublisher version
http://www.sciencedirect.com/science/article/pii/S0957417412009918Subject
Abstract
Efficient management of energy in buildings saves a very important amount of resources (both economic and technological). As a consequence, there is a very active research in this field. One of the keys of energy ... [+]
Efficient management of energy in buildings saves a very important amount of resources (both economic and technological). As a consequence, there is a very active research in this field. One of the keys of energy management is the prediction of the variables that directly affect building energy consumption and personal comfort. Among these variables, one can highlight the temperature in each room of a building. In this work we apply different machine learning techniques along with other classical ones for predicting the temperatures in different rooms. The obtained results demonstrate the validity of these techniques for predicting temperatures and, therefore, for the establishment of optimal policies of energy consumption. [-]
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Expert Systems with Applications, 2013, vol. 40, no 4Rights
Copyright © 2012 Elsevier Ltd. All rights reserved.
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- LSI_Articles [361]