Machine learning methods to forecast temperature in buildings

Authors:
Fernando Mateo;Juan José Carrasco;Abderrahim Sellami;MóNica MilláN-Giraldo;Manuel DomíNguez;Emilio Soria-Olivas
Affiliations:
Intelligent Data Analysis Laboratory, E.T.S.E, University of Valencia, Avda Universitat S/N, 46100 Burjassot, Valencia, Spain;Intelligent Data Analysis Laboratory, E.T.S.E, University of Valencia, Avda Universitat S/N, 46100 Burjassot, Valencia, Spain;Intelligent Data Analysis Laboratory, E.T.S.E, University of Valencia, Avda Universitat S/N, 46100 Burjassot, Valencia, Spain;Intelligent Data Analysis Laboratory, E.T.S.E, University of Valencia, Avda Universitat S/N, 46100 Burjassot, Valencia, Spain and Institute of New Imaging Technologies, Department of Computer Lang ...;SUPPRESS Research Group, University of León, University Campus de Vegazana, 24007 León, Spain;Intelligent Data Analysis Laboratory, E.T.S.E, University of Valencia, Avda Universitat S/N, 46100 Burjassot, Valencia, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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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 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.