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Transfer learning for thermal comfort prediction in multiple cities

机译：转移多个城市热舒适预测的学习

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摘要

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.
机译：HVAC（供暖，通风和空调）系统是建筑物的重要组成部分，其占建筑能源使用量的40％。 HVAC的主要目的是保持适当的热舒适性，对于最佳能量使用至关重要。此外，热舒适性对福祉，健康和工作率也很重要。最近，数据驱动的热舒适模型比传统知识的方法实现了更好的性能（例如，预测的平均投票模型）。精确的热舒适性模型需要来自室内居住者的大量自我报告的热舒适数据，无疑是研究人员挑战。在这项研究中，我们的目标是解决这种数据短缺问题并提高热舒适预测的性能。我们利用来自同一气候区域的多个城市的传感器数据来学习热舒适图案。我们从相同的气候区（TL-MLP-C *）上介绍了一种基于学习的多层的Perceptron模型，用于准确热舒适预测。在ASHRAE RP-884上进行了广泛的实验结果，秤项目和中式美国办事处数据集表明，所提出的TL-MLP-C *的性能超过最先进的方法的性能准确性和F1分数。

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