Evren Fatih, Biswas Sayan, Graves Richard
Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA.
Center for Sustainable Building Research, University of Minnesota Twin Cities, Minneapolis, MN, USA.
Nat Commun. 2025 Jan 31;16(1):1215. doi: 10.1038/s41467-024-55122-z.
Measuring and controlling human thermal perception-related parameters within the built environment is crucial for ensuring occupant comfort, productivity, well-being, and reduced energy consumption. The human body is sensitive to both convective and radiative thermal effects. Mean radiant temperature represents the comprehensive radiant thermal impact individuals perceive in their surroundings. However, no feasible, robust, and ergonomic methods exist for real-time mean radiant temperature measurements in the built environment. In this paper, we introduce a method for measuring longwave mean radiant temperature utilizing low-resolution infrared temperature sensors. The approach utilizes projective transformations to derive surface temperature distributions from raw infrared thermal data. Our technique is tested in four diverse real-world environments, encompassing different heating methods and room configurations, resulting in a maximum error of ±0.5 °C. The results demonstrate the method's repeatability and robustness across diverse room sizes, layouts, and scenarios, suggesting its potential integration into room thermostats to improve human comfort while optimizing building energy utilization. We anticipate that this method will revolutionize sensing in the built environment by eliminating the requirement for costly hardware.
在建筑环境中测量和控制与人体热感知相关的参数对于确保居住者的舒适度、生产力、幸福感以及降低能源消耗至关重要。人体对对流和辐射热效应都很敏感。平均辐射温度代表了个体在周围环境中所感知到的综合辐射热影响。然而，在建筑环境中，不存在可行、可靠且符合人体工程学的实时平均辐射温度测量方法。在本文中，我们介绍了一种利用低分辨率红外温度传感器测量长波平均辐射温度的方法。该方法利用射影变换从原始红外热数据中推导表面温度分布。我们的技术在四种不同的实际环境中进行了测试，涵盖不同的供暖方式和房间配置，最大误差为±0.5°C。结果表明该方法在不同房间尺寸、布局和场景下具有可重复性和鲁棒性，这表明它有可能集成到房间恒温器中，以提高人体舒适度，同时优化建筑能源利用。我们预计，这种方法将通过消除对昂贵硬件的需求，彻底改变建筑环境中的传感方式。
