Transfer Learning for Thermal Comfort Prediction in Multiple Cities
Abstrak
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.
Topik & Kata Kunci
Penulis (6)
Nan Gao
Wei Shao
M. Rahaman
Jun Zhai
K. David
Flora D. Salim
Akses Cepat
- Tahun Terbit
- 2020
- Bahasa
- en
- Total Sitasi
- 95×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1016/J.BUILDENV.2021.107725
- Akses
- Open Access ✓