A systematic literature review on smart and personalized ventilation using CO2 concentration monitoring and control

Review (2022)
Author(s)

Ge Song (Hunan University)

Zhengtao Ai (Hunan University)

Zhengxuan Liu (Hunan University, TU Delft - Design & Construction Management)

Guoqiang Zhang (Hunan University)

Research Group
Design & Construction Management
Copyright
© 2022 Ge Song, Zhengtao Ai, Zhengxuan Liu, Guoqiang Zhang
DOI related publication
https://doi.org/10.1016/j.egyr.2022.05.243
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Ge Song, Zhengtao Ai, Zhengxuan Liu, Guoqiang Zhang
Research Group
Design & Construction Management
Volume number
8
Pages (from-to)
7523-7536
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Smart and personalized ventilation systems have been demonstrated with high performance in creating a healthy and energy-efficient indoor environment, but they have been rarely comprehensively summarized and explored in previous studies. With the progressive development of various terminal devices and control technologies, personalized ventilation based on intelligent control is potentially a promising way to achieve efficient control and energy savings in human micro-environments. This study comprehensively summarizes and analyzes the recent studies and common utilization forms of smart ventilation and PV systems that are based on CO2 concentration control, to pave path and provide some guidelines for their integration application for reducing energy consumption and improving indoor thermal comfort. Research shows that the combination of personalized ventilation and smart ventilation is an essential development for ventilation systems. Smart ventilation with demand control logic based on CO2 concentration has been mature enough to effectively improve the effectiveness and comfortable performance of personalized ventilation. However, switching from traditional air conditioning systems to personalized ventilation still requires improved sensors and intelligent control algorithms. In addition, this paper also summarizes the exploratory studies and potential application analysis of machine-learning theories to improve intelligent control of personalized ventilation. To this end, this paper identifies future tendencies for advanced theories, integrated systems, and devices in personalized ventilation systems.