Transfer Learning for Thermal Comfort Prediction in Multiple Cities

Publication Year: 2020 Publication Type : JournalArticle


HVAC (Heating, Ventilation and Air Conditioning) system is an important part of a building, which constitutes up to 40% of building energy usage. The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage. Besides, thermal comfort is also crucial for well-being, health, and work productivity. Recently, data-driven thermal comfort models have got better performance than traditional knowledge-based methods (e.g. Predicted Mean Vote Model). An accurate thermal comfort model requires a large amount of self-reported thermal comfort data from indoor occupants which undoubtedly remains a challenge for researchers. In this research, we aim to tackle this data-shortage problem and boost the performance of thermal comfort prediction. We utilise sensor data from multiple cities in the same climate zone to learn thermal comfort patterns. We present a transfer learning based multilayer perceptron model from the same climate zone (TL-MLP-C*) for accurate thermal comfort prediction. Extensive experimental results on ASHRAE RP-884, the Scales Project and Medium US Office datasets show that the performance of the proposed TL-MLP-C* exceeds the state-of-the-art methods in accuracy and F1-score.


@article{DBLP:journals/corr/abs-2004-14382, archiveprefix = {arXiv},
    author = {Nan Gao and Wei Shao and Mohammad Saiedur Rahaman and Jun Zhai and Klaus David and Flora D. Salim},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    eprint = {2004.14382},
    journal = {CoRR},
    timestamp = {Sun, 03 May 2020 01:00:00 +0200},
    title = {Transfer Learning for Thermal Comfort Prediction in Multiple Cities},
    url = {},
    volume = {abs/2004.14382},
    year = {2020}


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