Data-driven thermal comfort modeling: Comparing AI-based predictions with PMV-PPD model

Accurate thermal comfort modeling is essential for optimizing energy-efficient, occupant-centric indoor environments. While widely used, traditional models such as Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) often fail to capture individual variability in thermal perception...

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Main Authors: Razak, Tajul Rosli, Jarimi, Hasila, Ismail, Mohammad Hafiz, Mohd Nadzir, Mohd Shahrul, Ahmad, Emy Zairah, Abd Rahman, Noor Muhamad, Jamaludin, Mohd Haikal, Yuehong, Su, Riffat, Saffa
Format: Article
Language:en
Published: Elsevier Ltd 2025
Online Access:http://eprints.utem.edu.my/id/eprint/29184/2/0228218092025113452116.pdf
http://eprints.utem.edu.my/id/eprint/29184/
https://pdf.sciencedirectassets.com/271089/1-s2.0-S0378778825X00207/1-s2.0-S0378778825011405/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGIaCXVzLWVhc3QtMSJGMEQCICdmvH59%2BFZCz5iCAxf%2BCXiLL6%2F%2FdXrfEVhijMf9DaZuAiB3CO58cfn5kq%2BDEldbottK4n7o8BV0JgQmbCAJqzteISqzBQgrEAUaDDA1OTAwMzU0Njg2NSIMY0Y7rOQavuCfKTeOKpAFxiftySDsbdECoAo2BVq8kcGArwV7Dr1NVh1QxSHEXgIgTeoL%2Bpjw7H59MF3WMhGAYJUZyPiLk%2BKV5Ab2e3WmJb%2FH2GzEVCPhdLgVjAxd0DbVqEjaK5QuC7dfHJqKSjzyDqfn7BGKDkTtV2X2LqcV4OrIaR8h864u1%2BudUyV6%2FGbKVcSCP3tShTkOxPSlHQP3%2F8NJzTgd9drZe%2FEnoaCW0WBi1wyu87LxVJga30lHott3%2FK6185egb2YwxwFd9gLg96CD0bY7PQgiywbhh8vtRqe4d7iZC8Dsu8p8Bl2W1I4e%2FQPsiTvoYEghtbqyqOOVd3v%2BMB5Xz16yBu%2Bt0%2B5j4I%2FDXdvGDqs%2BCncWdRcWIWSXGplLw2id0qptH6DvOzy5Q%2B8guRgsfhrsITFT5NTIQZRkjefYprZ2iU5Ye1c3LMP1kSJ%2F4yC2ZMDD8kAqeFlh8QSR2gBj47Q7yWDD2yT5L5lhUDUrDIKqF6XmsxLYEiztq8JRoi3lkKBYWSS46V6wiTMSexY1kTeSnaaUbJITDzLezET5gZ2JbfGoKzLM%2BxKMm21if7MOSgPoUU0GelF2b4fGGqro6JfA78RTlPDRaN91GnhPXMlGM3ynssM0ERujuiGx6b6n6ZA5TMi7P7l2%2FWb2mF7ET8v1cSuBol6EpZwCFeyb3u9%2FiJbZQhU3cn8qV0EmOKEFIIX7trqg7wlvYu9VTVShleJOyg5ThY%2BWT3F0JNtUGf86F0mM4Z8mWK5%2FGy0BECmeigsz7SUw7QF6ZjyNVJXvgdEeUGfBB%2F7A%2FMfKi5A32M19pVTj5Ub4uyB6tENbfQeKja8DLkVsvh3AEGoKSkszMu%2FmRS%2Bvy%2Bn8qoNRoF4y7oJxBqH07CdRB9kwisrPyAY6sgERmPm0aQtEEl7LNbsCs00QCTuxbxjmmfF51dFBt%2Fk%2Bn%2BMMqK2kGztgCPyaJLqZQuEe%2FI900mz2kgTxHAZYTdWCCm9Maao7LCXVD4UrVIsv6cTDt%2BpD3sougJEdHMzIgIm0Y265VzfKDtlPlQKlCD%2FkXT5vSB7efRvEKDPcmusJ9Os%2BLm9hgrKwc0vw9OBfVIy6vyTj8CLvis3NhgFS7H62sN1zuIZfaHoLEJvgNcCOY26Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20251112T015644Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYZJPP3AFW%2F20251112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=53515b42935a66bc6b08f73ee91be136d90380cd5ed3b97959de8c820dfc9db1&hash=d6b05914e9e31a966718a102d049145b0e8ec9fadc9bd6f75ce9db0d5fc68115&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0378778825011405&tid=spdf-784bbfd8-196a-4491-83d3-e3a9af03b79a&sid=dee71bdf306af944b54935f76a77bd2d5843gxrqb&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&rh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=171d5c5a0b5c59065150&rr=99d26a222883cac4&cc=my
https://doi.org/10.1016/j.enbuild.2025.116410
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