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Lookup NU author(s): Dr Neveen Hamza
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The current outdoor thermal comfort index assessment is either based on thermal sensation votes collected through field surveys/questionnaires or using equations fundamentally backed by thermodynamics, such as the widely used UTCI and PET indices. The predictive ability of all methods suffers from discrepancies as multi-sensory attributes, cultural, emotional, and psychological cognition factors are ignored. These factors are proven to influence the thermal sensation and duration people spend outdoors, and are equally prominent factors as air temperature, solar radiation, and relative humidity. The studies that adopted machine learning models, such as Artificial Neural Networks (ANNs), concentrated on improving the predictive capability of PET, thereby making the field of Artificial Intelligence (AI) domain underexplored. Furthermore, universally adopted outdoor thermal comfort indices under-predict a neutral thermal range, for a reason that is linked to the fact that all indices were validated on European/American subjects living in temperate, cold regions. The review highlighted gaps and challenges in outdoor thermal comfort prediction accuracy by comparing traditional methods and Industry 4.0. Additionally, a further recommendation to improve prediction accuracy by exploiting Industry 4.0 (machine learning, artificial reality, brain–computer interface, geo-spatial digital twin) is examined through Knowledge Translation
Author(s): Elnabawi M, Hamza N
Publication type: Article
Publication status: Published
Journal: Buildings
Year: 2024
Volume: 14
Issue: 4
Print publication date: 25/03/2024
Online publication date: 25/03/2024
Acceptance date: 18/03/2024
Date deposited: 26/03/2024
ISSN (electronic): 2075-5309
Publisher: MDPI
URL: https://doi.org/10.3390/buildings14040879
DOI: 10.3390/buildings14040879
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