Meta-learning of personalized thermal comfort model and fast identification of the best personalized thermal environmental conditions
Meta-learning of personalized thermal comfort model and fast identification of the best personalized thermal environmental conditions
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DOI：
10.1016/j.buildenv.2023.110201
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发表时间：
2023-05
影响因子：
7.4
通讯作者：
Liangliang Chen;Ayca Ermis;Fei Meng;Ying Zhang
中科院分区：
文献类型：
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作者：
Liangliang Chen;Ayca Ermis;Fei Meng;Ying Zhang
个性化热舒适度的模型可以通过各种机器学习算法来学习，并用于提高个人的热舒适度水平，同时可能降低HVAC系统的能耗。然而，这种模型的学习通常需要来自所考虑的乘员的大量热投票，并且为了获得具有良好泛化能力的模型，收集一些投票所需的环境条件可能是乘员不期望的。在本文中，我们建议使用元学习算法来减少所需的个性化热投票的数量，使个性化的热舒适性模型可以得到只有少量的反馈。通过学习的元模型，我们推导出一种基于神经网络反向传播的方法，以快速识别特定乘员的最佳环境和个人条件。所提出的识别算法具有一个额外的优点，即由平均热感觉值表示的热舒适性在数据收集过程中逐步提高。我们使用ASHRAE全球热舒适数据库II验证元学习算法可以实现一个更高的预测精度后，使用5个热感觉投票从乘员进行调整。此外，我们展示了最佳个性化热环境条件的快速识别算法的有效性与热感觉生成模型从PMV模型建立。
The model of personalized thermal comfort can be learned via various machine learning algorithms and used to improve the individuals’ thermal comfort levels with potentially less energy consumption of HVAC systems. However, the learning of such a model typically requires a substantial number of thermal votes from the considered occupant, and the environmental conditions needed for collecting some votes may be undesired by the occupant in order to obtain a model with good generalization ability. In this paper, we propose to use a meta-learning algorithm to reduce the required number of personalized thermal votes so that a personalized thermal comfort model can be obtained with only a small number of feedback. With the learned meta-model, we derive a method based on the backpropagation of neural networks to quickly identify the best environmental and personal conditions for a specific occupant. The proposed identification algorithm has an additional advantage that the thermal comfort, indicated by the mean thermal sensation value, improves incrementally during the data collection process. We use the ASHRAE global thermal comfort database II to verify that the meta-learning algorithm can achieve an improved prediction accuracy after using 5 thermal sensation votes from an occupant to make adaptations. In addition, we show the effectiveness of the fast identification algorithm for the best personalized thermal environmental conditions with a thermal sensation generation model built from the PMV model.