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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Antonella Yaacoub & Moez Esseghir & Leila Merghem-Boulahia, 2023. "A Review of Different Methodologies to Study Occupant Comfort and Energy Consumption," Energies, MDPI, vol. 16(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1634-:d:1059971
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    References listed on IDEAS

    as
    1. Naja Aqilah & Hom Bahadur Rijal & Sheikh Ahmad Zaki, 2022. "A Review of Thermal Comfort in Residential Buildings: Comfort Threads and Energy Saving Potential," Energies, MDPI, vol. 15(23), pages 1-23, November.
    2. Christina Turley & Margarite Jacoby & Gregory Pavlak & Gregor Henze, 2020. "Development and Evaluation of Occupancy-Aware HVAC Control for Residential Building Energy Efficiency and Occupant Comfort," Energies, MDPI, vol. 13(20), pages 1-30, October.
    3. Kong, Meng & Dong, Bing & Zhang, Rongpeng & O'Neill, Zheng, 2022. "HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study," Applied Energy, Elsevier, vol. 306(PA).
    4. Michael M. Santos & Ana Vaz Ferreira & João C. G. Lanzinha, 2022. "Passive Solar Systems for the Promotion of Thermal Comfort in African Countries: A Review," Energies, MDPI, vol. 15(23), pages 1-37, December.
    5. Djongyang, Noël & Tchinda, René & Njomo, Donatien, 2010. "Thermal comfort: A review paper," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2626-2640, December.
    6. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Li, Wenqiang & Peng, Pei, 2021. "A hybrid deep transfer learning strategy for short term cross-building energy prediction," Energy, Elsevier, vol. 215(PB).
    7. Giulia Lamberti & Giacomo Salvadori & Francesco Leccese & Fabio Fantozzi & Philomena M. Bluyssen, 2021. "Advancement on Thermal Comfort in Educational Buildings: Current Issues and Way Forward," Sustainability, MDPI, vol. 13(18), pages 1-29, September.
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