@inproceedings{a91a3464ca024d4fb1be69bff469cad7,
title = "Regression model of wet-bulb temperature in an HVAC system",
abstract = "It can result in substantial energy saving in heating, ventilation, and air-conditioning (HVAC) system by improving the control strategy of heating, ventilation, and air-conditioning system. However, it is challenging to obtain the optimal control strategy of an HVAC system due to its model{\textquoteright}s complexity. In this paper, a regression model is proposed for the wet-bulb temperature which is a key variable in cooling tower and fan coil unit. The proposed model avoids the iterative computing process of obtaining the value of the wet-bulb temperature and reduces the complexity of an HVAC system{\textquoteright}s model. Numerical results show that the proposed model takes less than 7\% computing time to get the value of wet-bulb temperature, and the relative deviations are less than 0.4\%, compared to the original model.",
keywords = "HVAC system, Regression model, Wet-bulb temperature",
author = "Luping Zhuang and Xi Chen and Xiaohong Guan",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; International Conference on Smart City and Intelligent Building, ICSCIB 2018 ; Conference date: 15-09-2018 Through 16-09-2018",
year = "2019",
doi = "10.1007/978-981-13-6733-5\_18",
language = "英语",
isbn = "9789811367328",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "197--205",
editor = "Quanmin Zhu and Feng Qiao and Qiansheng Fang",
booktitle = "Advancements in Smart City and Intelligent Building - Proceedings of the International Conference on Smart City and Intelligent Building ICSCIB 2018",
}