Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A review of building occupancy measurement systems
Highlights We focus on building occupancy information measurement for energy control and building performance, and review many recent especially five-years studies. Many occupancy measurement systems based on different sensors, cameras, WiFi, PIR sensors, CO2 sensors, electricity sensors are analyzed and discussed. Due to the rapid development of occupancy measurement based on cameras, we conduct a comprehensive analysis based on different types (non-depth or depth cameras) and different installed locations (the room entrance or interior) of cameras. Some future trends are presented, including sensor fusion methods, cloud computing platform, and deep learning technologies.
Abstract The human dimension information is crucial for efficient building energy saving, health and productivity, comfort conditions and security management. A great number of studies have been developed for occupancy information measurement. However, existing review work has limited coverage on emerging images/videos based methods. In this paper, many occupancy measurement systems based on different sensors are reviewed, especially images/videos based methods. We conduct a comprehensive analysis based on different types (non-depth or depth cameras) and different installed locations (the room entrance or interior) of cameras. Considering the motion and static image information, we categorize these studies and compare the merits and limitations. As for other sensors, Wireless Fidelity (WiFi), Passive Infrared (PIR) sensors, carbon dioxide (CO2) sensors, electricity meters, this paper analyzes and discusses their applicable scopes and limitations. Sensor fusion method tends to perform better because different sensors can compensate each other. Moreover, future trends are presented, including the fifth generation mobile network (5 G), cloud computing platform and artificial intelligence especially deep learning technologies
A review of building occupancy measurement systems
Highlights We focus on building occupancy information measurement for energy control and building performance, and review many recent especially five-years studies. Many occupancy measurement systems based on different sensors, cameras, WiFi, PIR sensors, CO2 sensors, electricity sensors are analyzed and discussed. Due to the rapid development of occupancy measurement based on cameras, we conduct a comprehensive analysis based on different types (non-depth or depth cameras) and different installed locations (the room entrance or interior) of cameras. Some future trends are presented, including sensor fusion methods, cloud computing platform, and deep learning technologies.
Abstract The human dimension information is crucial for efficient building energy saving, health and productivity, comfort conditions and security management. A great number of studies have been developed for occupancy information measurement. However, existing review work has limited coverage on emerging images/videos based methods. In this paper, many occupancy measurement systems based on different sensors are reviewed, especially images/videos based methods. We conduct a comprehensive analysis based on different types (non-depth or depth cameras) and different installed locations (the room entrance or interior) of cameras. Considering the motion and static image information, we categorize these studies and compare the merits and limitations. As for other sensors, Wireless Fidelity (WiFi), Passive Infrared (PIR) sensors, carbon dioxide (CO2) sensors, electricity meters, this paper analyzes and discusses their applicable scopes and limitations. Sensor fusion method tends to perform better because different sensors can compensate each other. Moreover, future trends are presented, including the fifth generation mobile network (5 G), cloud computing platform and artificial intelligence especially deep learning technologies
A review of building occupancy measurement systems
Sun, Kailai (Autor:in) / Zhao, Qianchuan (Autor:in) / Zou, Jianhong (Autor:in)
Energy and Buildings ; 216
12.03.2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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