Department of Architectural Engineering, Sejong University, 209 Neungdong-Ro, Gwangjin-Gu, Seoul 05006, Republic of Korea.
Department of Architectural Engineering, Dankook University, Youngin 16890, Republic of Korea.
Sensors (Basel). 2023 Jan 4;23(2):585. doi: 10.3390/s23020585.
Infectious diseases such as the COVID-19 pandemic have necessitated preventive measures against the spread of indoor infections. There has been increasing interest in indoor air quality (IAQ) management. Air quality can be managed simply by alleviating the source of infection or pollution, but the person within a space can be the source of infection or pollution, thus necessitating an estimation of the exact number of people occupying the space. Generally, management plans for mitigating the spread of infections and maintaining the IAQ, such as ventilation, are based on the number of people occupying the space. In this study, carbon dioxide (CO)-based machine learning was used to estimate the number of people occupying a space. For machine learning, the CO concentration, ventilation system operation status, and indoor-outdoor and indoor-corridor differential pressure data were used. In the random forest (RF) and artificial neural network (ANN) models, where the CO concentration and ventilation system operation modes were input, the accuracy was highest at 0.9102 and 0.9180, respectively. When the CO concentration and differential pressure data were included, the accuracy was lowest at 0.8916 and 0.8936, respectively. Future differential pressure data will be associated with the change in the CO concentration to increase the accuracy of occupancy estimation.
传染病，如 COVID-19 大流行，需要采取预防措施来防止室内感染的传播。人们对室内空气质量（IAQ）管理越来越感兴趣。通过缓解感染源或污染源，空气质量可以得到简单的管理，但空间内的人可能是感染源或污染源，因此需要准确估计占用空间的人数。通常，为了减轻感染的传播和保持室内空气质量，如通风，而制定的管理计划是基于占用空间的人数。在这项研究中，基于二氧化碳（CO）的机器学习被用于估计占用空间的人数。对于机器学习，使用了 CO 浓度、通风系统运行状态以及室内外和室内走廊压差数据。在随机森林（RF）和人工神经网络（ANN）模型中，输入 CO 浓度和通风系统运行模式，其准确性分别最高为 0.9102 和 0.9180。当包含 CO 浓度和压差数据时，准确性分别最低为 0.8916 和 0.8936。未来将关联压差数据与 CO 浓度的变化，以提高占用人数的估计准确性。
