Transfer learning for thermal comfort prediction in multiple cities.pdf (1.3 MB)
Transfer learning for thermal comfort prediction in multiple cities
journal contributionposted on 2023-12-20, 01:10 authored by N Gao, W Shao, Mohammad Saiedur Rahaman, J Zhai, K David, FD Salim
The 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 energy usage. Additionally, thermal comfort is also important for well-being, health, and work productivity. Recently, data-driven thermal comfort models have achieved better performance than traditional knowledge-based methods (e.g. the 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 address this data-shortage problem and boost the performance of thermal comfort prediction. We utilize 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 the ASHRAE RP-884, Scales Project and Medium US Office datasets show that the performance of the proposed TL-MLP-C* exceeds the performance of state-of-the-art methods in accuracy and F1-score.
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
Number of Pages12
Additional RightsCC BY-NC-ND 4.0