Transfer Learning for Thermal Comfort Prediction in Multiple Cities

• URL: http://arxiv.org/abs/2004.14382v3
• Date: Wed, 21 Oct 2020 00:14:52 GMT
• Title: Transfer Learning for Thermal Comfort Prediction in Multiple Cities
• Authors: Nan Gao, Wei Shao, Mohammad Saiedur Rahaman, Jun Zhai, Klaus David, Flora D. Salim
• Abstract summary: This research aims to tackle the 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 for accurate thermal comfort prediction.
• Score: 8.759740337781526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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