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.

HVAC（供暖，通风和空调）系统是建筑物的重要组成部分，占建筑物能耗的40％。保持适当的热舒适性的HVAC的主要目的对于最佳利用能源至关重要。此外，热舒适性对于健康，健康和工作效率也很重要。最近，数据驱动的热舒适模型比传统的基于知识的方法（例如，预测的平均投票模型）取得了更好的性能。准确的热舒适模型需要室内人员提供大量自我报告的热舒适数据，这无疑对研究人员仍然构成挑战。在这项研究中，我们旨在解决此数据短缺问题并提高热舒适性预测的性能。我们利用来自同一气候区中多个城市的传感器数据来学习热舒适度模式。我们提出了来自同一气候区（TL-MLP-C *）的基于转移学习的多层感知器模型，用于准确的热舒适性预测。在ASHRAE RP-884，Scales Project和Medium US Office数据集上的大量实验结果表明，所提出的TL-MLP-C *的性能在准确性和F1得分方面超过了最新方法。