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首页> 外文期刊>International Journal of Distributed Sensor Networks >IoT-based personal thermal comfort control for livable environment
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IoT-based personal thermal comfort control for livable environment

机译：适用于宜居环境的基于IoT的个人热舒适控制

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Thermal comfort control for indoor environment has become an important issue in smart cities since it is beneficial for people’s health and helps to maximize their working productivity and to provide a livable environment. In this article, we present an Internet of things–based personal thermal comfort model with automatic regulation. This model employs some environment sensors such as temperature sensor and humidity sensor to continuously obtain the general environmental measurements. Specially, video cameras are also integrated into the Internet of things network of sensors to capture the individual’s activity and clothing condition, which are important factors affecting one’s thermal sensation. The individual’s condition image can be mapped into different metabolic rates and different clothing insulations by machine learning classification algorithm. Then, all the captured or converted data are fed into a predicted mean vote model to learn the individual’s thermal comfort level. In the prediction stage, we introduce the cuckoo search algorithm, which converges rapidly, to solve the air temperature and air velocity with the learnt thermal comfort level. Our experiments demonstrate that the metabolic rates and clothing insulation have great effect on personal thermal comfort, and our model with video capture helps to obtain the variant values regularly, thus maintains the individual’s thermal comfort balance in spite of the variations in individual’s activity or clothing.
机译：室内环境的热舒适控制已成为智慧城市中的重要问题，因为它对人们的健康有益，并有助于最大化他们的工作效率并提供宜居的环境。在本文中，我们介绍了具有自动调节功能的基于物联网的个人热舒适模型。该模型使用一些环境传感器（例如温度传感器和湿度传感器）来连续获取常规环境测量值。特别地，摄像机还集成到传感器的物联网中，以捕获个人的活动和衣着状况，这是影响个人热感的重要因素。通过机器学习分类算法，可以将个人的状况图像映射到不同的代谢率和不同的衣物隔热层。然后，将所有捕获或转换的数据输入到预测的平均投票模型中，以了解个人的热舒适程度。在预测阶段，我们引入了布谷鸟搜索算法，该算法快速收敛，以具有学习到的热舒适水平的方式来解决气温和风速。我们的实验表明，新陈代谢的速度和衣服的隔热性能对个人的热舒适度有很大影响，我们的带有视频捕获功能的模型有助于定期获取变化值，从而尽管个人活动或衣服有所不同，但仍可保持个人的热舒适平衡。

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