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Study of Human Thermal Comfort for Cyber–Physical Human Centric System in Smart Homes

机译:智能家居中以网络为中心的人体中心系统的人体热舒适性研究

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摘要

An environmental thermal comfort model has previously been quantified based on the predicted mean vote (PMV) and the physical sensors parameters, such as temperature, relative humidity, and air speed in the indoor environment. However, first, the relationship between environmental factors and physiology parameters of the model is not well investigated in the smart home domain. Second, the model that is not mainly for an individual human model leads to the failure of the thermal comfort system to fulfill the human’s comfort preference. In this paper, a cyber–physical human centric system (CPHCS) framework is proposed to take advantage of individual human thermal comfort to improve the human’s thermal comfort level while optimizing the energy consumption at the same time. Besides that, the physiology parameter from the heart rate is well-studied, and its correlation with the environmental factors, i.e., PMV, air speed, temperature, and relative humidity are deeply investigated to reveal the human thermal comfort level of the existing energy efficient thermal comfort control (EETCC) system in the smart home environment. Experimental results reveal that there is a tight correlation between the environmental factors and the physiology parameter (i.e., heart rate) in the aspect of system operational and human perception. Furthermore, this paper also concludes that the current EETCC system is unable to provide the precise need for thermal comfort to the human’s preference.
机译:先前已经基于预测的平均投票(PMV)和物理传感器参数(例如室内环境中的温度,相对湿度和空气速度)对环境热舒适模型进行了量化。但是,首先,在智能家居领域,尚未很好地研究环境因素与模型的生理参数之间的关系。其次,并非主要针对个人人体模型的模型导致热舒适系统无法满足人体的舒适偏好。在本文中,提出了一个以网络为物理的以人为中心的系统(CPHCS)框架,以利用个人的人体热舒适度来改善人体的热舒适度,同时优化能耗。除此之外,还对心率的生理参数进行了深入研究,并对其与环境因素(即PMV,空气速度,温度和相对湿度)的相关性进行了深入研究,以揭示现有节能设备的人体热舒适水平。智能家居环境中的热舒适控制(EETCC)系统。实验结果表明,在系统操作和人类感知方面,环境因素与生理参数(即心率)之间存在紧密的相关性。此外,本文还得出结论,当前的EETCC系统无法满足人们的喜好,提供对热舒适性的精确需求。

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