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Machine learning practices during the operational phase of buildings: a critical review

Authors:

Lizny Jaufer1

,

 Shuraik Kader2

,

 Velibor Spalevic3

,

 Goran Skataric4

,

Branislav Dudic5

1School of Architecture, Liverpool John Moores University, Merseyside L3 5UX, United Kingdom
2School of Engineering and Built Environment, Griffith University, 170 Kessels road, QLD 4111, Australia
3Biotechnical Faculty, University of Montenegro, Podgorica 81000, Montenegro
4Management, Faculty of Maritime Studies and Faculty of Sport and Physical Education, University of Montenegro, Montenegro
5Faculty of Management, Comenius University Bratislava, 81499 Bratislava, Slovakia

Received: 16 December 2023
Revised: 2 March 2024
Accepted: 17 March 2024
Published: 31 March 2024

Abstract:

Machine Learning (ML) is gaining attention in civil engineering especially within operational phase of building life cycle. This phase is crucial for managing every energy aspect while ensuring occupant comfort. Previous ML experiments have explored occupant behavior, occupancy estimation, load prediction, defect detection, and Heating, Ventilation, and Air Conditioning (HVAC) system diagnostics. However, challenges such as ML transferability and limited literature on ML components for the operational phase hinder broader industry adoption. This critical review aims to assess the potential of ML in building operations, focusing on energy consumption, big data control, reinforcement learning, and thermal comfort modeling. By identifying knowledge gaps, the study recommends further research to leverage ML for sustainable energy consumption and occupant comfort. It highlights ML’s promising role in striking a balance between energy efficiency and occupant wellbeing.

Keywords:

Big data control, Energy consumption, Operational phase, Reinforcement learning, Thermal comfort modeling

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© 2024 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)

Volume 9
Number 1
March 2024

Last Edition

Volume 9
Number 1
March 2024

How to Cite

L. Jaufer, S. Kader, V. Spalevic, G. Skataric, B. Dudic, Machine Learning Practices During the Operational Phase of Buildings: A Critical Review. Applied Engineering Letters, 9(1), 2024: 37-45.
https://doi.org/10.46793/aeletters.2024.9.1.4

More Citation Formats

Jaufer, L., Kader, S., Spalevic, V., Skataric, G., & Dudic, B. (2024). Machine Learning Practices During the Operational Phase of Buildings: A Critical Review. Applied Engineering Letters, 9(1), 37-45.
https://doi.org/10.46793/aeletters.2024.9.1.4

Jaufer, Lizny, et al. “Machine Learning Practices During the Operational Phase of Buildings: A Critical Review.” Applied Engineering Letters, vol. 9, no. 1, 2024, pp. 37-45.
https://doi.org/10.46793/aeletters.2024.9.1.4

Jaufer, Lizny, Shuraik Kader, Velibor Spalevic, Goran Skataric, Branislav Dudic. 2024. “Machine Learning Practices During the Operational Phase of Buildings: A Critical Review.” Applied Engineering Letters, 9 (1): 37-45.
https://doi.org/10.46793/aeletters.2024.9.1.4

Jaufer, L., Kader, S., Spalevic, V., Skataric, G. and Dudic, B. (2024). Machine Learning Practices During the Operational Phase of Buildings: A Critical Review. Applied Engineering Letters, 9(1), pp. 37-45.
doi: 10.46793/aeletters.2024.9.1.4.