2024-03-29T12:40:43Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/904982020-09-15T12:35:54Zcom_10234_7038com_10234_9col_10234_8634
Repositori UJI
author
Mateo, Fernando
author
Carrasco, Juan José
author
Sellami, Abderrahim
author
Millán Giraldo, Mónica
author
Domínguez, Manuel
author
Soria Olivas, Emilio
2014-04-16T14:55:51Z
2014-04-16T14:55:51Z
2013
Expert Systems with Applications Volume 40, Issue 4, March 2013, Pages 1061–1068
0957-4174
http://hdl.handle.net/10234/90498
http://dx.doi.org/10.1016/j.eswa.2012.08.030
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.
eng
Copyright © 2012 Elsevier Ltd. All rights reserved.
Forecasting
Energy efficiency
Machine learning
Time series
Machine learning methods to forecast temperature in buildings
info:eu-repo/semantics/article
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