Efforts have been made to estimate clothing insulation in real time, an element of thermal comfort for occupants. Nevertheless, an effective method to estimate clothing insulation in real time is lacking. In addition, there has been little debate on how to apply clothing insulation to building control in practice. The purpose of this study is to propose a method for estimating clothing insulation using deep learning-based vision recognition, which has recently attracted attention and implement building control based on clothing insulation. The study also evaluates the significance of the method in effective building control. The results demonstrated that the proposed framework, CloNet, showed an accuracy of 94% for the validation image dataset and 86% for the actual built environment. In addition, we proved that the proposed vision-based estimation method is very fast and practical for estimating clothing insulation. The control experiment showed that the CloNet-based predicted mean vote (PMV) control changed the set temperature in response to changes in the subject's clothing. Compared to the traditional PMV control, the CloNet-based PMV control improved the thermal preference and thermal comfort vote. These results prove that clothing insulation estimation can be useful for building control.

已经努力实时估计衣服的隔热性，这是居住者热舒适性的一个因素。然而，缺乏一种实时估计服装绝缘的有效方法。此外，在实践中如何将服装隔热应用于建筑控制几乎没有争论。本研究的目的是提出一种使用基于深度学习的视觉识别来估计服装绝缘的方法，该方法最近引起了人们的关注并实施了基于服装绝缘的建筑控制。该研究还评估了该方法在有效建筑控制中的重要性。结果表明，所提出的框架 CloNet 对验证图像数据集的准确率为 94%，对实际建筑环境的准确率为 86%。此外，我们证明了所提出的基于视觉的估计方法对于估计服装绝缘非常快速和实用。控制实验表明，基于 CloNet 的预测平均投票 (PMV) 控制会根据受试者衣服的变化改变设定温度。与传统的 PMV 控制相比，基于 CloNet 的 PMV 控制改进了热偏好和热舒适投票。这些结果证明服装绝缘估计可用于建筑控制。基于 CloNet 的 PMV 控制改进了热偏好和热舒适投票。这些结果证明服装绝缘估计可用于建筑控制。基于 CloNet 的 PMV 控制改进了热偏好和热舒适投票。这些结果证明服装绝缘估计可用于建筑控制。