A Review of Different Methodologies to Study Occupant Comfort and Energy Consumption

Antonella Yaacoub (), Moez Esseghir and Leila Merghem-Boulahia
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Antonella Yaacoub: Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, 10300 Troyes, France
Moez Esseghir: Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, 10300 Troyes, France
Leila Merghem-Boulahia: Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, 10300 Troyes, France

Energies, 2023, vol. 16, issue 4, 1-18

Abstract: The goal of this work is to give a full review of how machine learning (ML) is used in thermal comfort studies, highlight the most recent techniques and findings, and lay out a plan for future research. Most of the researchers focus on developing models related to thermal comfort prediction. However, only a few works look at the current state of adaptive thermal comfort studies and the ways in which it could save energy. This study showed that using ML control schemas to make buildings more comfortable in terms of temperature could cut energy by more than 27%. Finally, this paper identifies the remaining difficulties in using ML in thermal comfort investigations, including data collection, thermal comfort indices, sample size, feature selection, model selection, and real-world application.

Keywords: thermal comfort; thermal sensation; machine learning; energy saving (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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