  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, precision and F1-score.
