Use of Machine Learning Methods for Indoor Temperature Forecasting

Lara Ramadan, Isam Shahrour, Hussein Mroueh and Fadi Hage Chehade
Additional contact information
Lara Ramadan: Laboratoire Génie Civil et Géo-Environnement, University Lille, IMT Lille Douai, JUNIA Hauts de France, ULR 4515–LGCgE, 59000 Lille, France
Isam Shahrour: Laboratoire Génie Civil et Géo-Environnement, University Lille, IMT Lille Douai, JUNIA Hauts de France, ULR 4515–LGCgE, 59000 Lille, France
Hussein Mroueh: Laboratoire Génie Civil et Géo-Environnement, University Lille, IMT Lille Douai, JUNIA Hauts de France, ULR 4515–LGCgE, 59000 Lille, France
Fadi Hage Chehade: Modeling Center, Doctoral School of Science and Technology, Lebanese University, Hadath 99000, Lebanon

Future Internet, 2021, vol. 13, issue 10, 1-18

Abstract: Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model.

Keywords: energy efficiency; prediction; indoor temperature; machine learning; gray box model (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1999-5903/13/10/242/pdf (application/pdf)
https://www.mdpi.com/1999-5903/13/10/242/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:13:y:2021:i:10:p:242-:d:641469

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().