Abstract
Thermal Comfort Control for indoor environment is an important issue in smart city since it is benefit to people’s health and helps to maximize their working productivity and provide a livable environment. In this paper, we present an IOT (Internet of Things) based personal thermal comfort model with automatic regulation. This model employs some environment sensors such as temperature sensor, humidity sensor, etc., to continuously obtain the general environmental measurements. Specially, video cameras are also integrated into the IOT network of sensors to capture the individual’s activity and dressing 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 PMV (Predicted Mean Vote) model to learn the individual’s thermal comfort level. In the prediction stage, we introduce the cuckoo search algorithm to solve the air temperature and air velocity with the learnt thermal comfort level, which is convergent rapidly. 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 variation of activity or clothing.
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Acknowledgements
This work is supported by Chinese National Natural Science Foundation (61771169), science and technology project of Beijing Municipal Education Commission (KM201510009005) and the excellent youthful teacher project of North China University of Technology (XN019006).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zang, M., Xing, Z., Tan, Y. (2019). IOT-Based Thermal Comfort Control for Livable Environment. In: Han, S., Ye, L., Meng, W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 286. Springer, Cham. https://doi.org/10.1007/978-3-030-22968-9_32
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DOI: https://doi.org/10.1007/978-3-030-22968-9_32
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