Abstract
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.
Highlights
Nowadays, people spend most of their time in enclosed environment, especially vulnerable seniors and younger population.[1]
It captures the capabilities of Internet of things (IoT) network to incorporate video camera as well as normal environment sensors together, where the video camera is used to capture the individual’s activity and clothing condition regularly, which will be converted into metabolic rate and clothing insulation by machine learning algorithm
We present a personal thermal comfort control model for indoor environment, which integrates the video camera with conventional sensors into an IoT network for data acquisition
Summary
People spend most of their time in enclosed environment, especially vulnerable seniors and younger population.[1]. Inspired by the previous work, we propose a thermal comfort control scheme for personal thermal balance adjustment with automation It captures the capabilities of Internet of things (IoT) network to incorporate video camera as well as normal environment sensors together, where the video camera is used to capture the individual’s activity and clothing condition regularly, which will be converted into metabolic rate and clothing insulation by machine learning algorithm . The main contributions in this article include the following: (1) we propose an optimal PMV thermal comfort control strategy, which introduces video camera to capture the images of individual’s activity and clothing periodically, learns the metabolic rate and clothing insulation for PMV calculation by machine learning method, and gives the corresponding adjustment for thermal comfort in time. We perform our experiments on the MATLAB2017b in a PC with 2.6 GHz CPU and 16 GB memory
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More From: International Journal of Distributed Sensor Networks
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