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A platform for research: civil engineering, architecture and urbanism
A novel occupant-centric stratum ventilation system using computer vision: Occupant detection, thermal comfort, air quality, and energy savings
https://orcid.org/0000-0003-4015-199X
https://orcid.org/0000-0002-5760-2147
https://orcid.org/0000-0002-2945-4685
Abstract Traditional ventilation and air conditioning systems typically operate on a predetermined schedule with fixed operating parameters. Occupant-centric control (OCC) strategies have been proposed to reduce system operation energy consumption without sacrificing thermal comfort. Indoor occupancy detection in real time is a critical step in successfully implementing the OCC strategy. Thus, the deep learning-based computer vision method was adopted in the first step of the study, and the detection performance and camera position were analyzed in an office scenario. Next, the proposed OCC strategy was used to regulate the supply air parameters and outdoor air volume in stratum ventilation based on the monitored occupant number. The traditional static control strategy was then compared to two control strategies: constant air volume and variable air volume. Occupant detection performance results showed the mean NRMSD for the five most common relative positions of the occupants and camera was 0.1109, with sitting back to camera having the lowest accuracy. Subjective response results demonstrated that, when compared to the traditional control strategy, thermal comfort was improved by 43%–73%, perceived air quality was maintained at an acceptable level, CO2 concentration was less than 700 ppm, and energy could be saved by 2.3%–8.1%. Furthermore, the lower the occupancy, the greater the improvement in comfort and the greater the energy savings. This research focused on how the stratum ventilation system responds to dynamic changes in occupancy and provided insights into reducing unnecessary energy waste while maintaining comfort.
Graphical abstract Display Omitted
Highlights Computer vision is used to detect the number of occupants in real-time. The proposed OCC strategy dynamically regulates the stratum ventilation system. The OCC-based stratum ventilation can improve thermal comfort and save energy. The CO2 concentration is lower than 700 ppm.
A novel occupant-centric stratum ventilation system using computer vision: Occupant detection, thermal comfort, air quality, and energy savings
https://orcid.org/0000-0003-4015-199X
https://orcid.org/0000-0002-5760-2147
https://orcid.org/0000-0002-2945-4685
Abstract Traditional ventilation and air conditioning systems typically operate on a predetermined schedule with fixed operating parameters. Occupant-centric control (OCC) strategies have been proposed to reduce system operation energy consumption without sacrificing thermal comfort. Indoor occupancy detection in real time is a critical step in successfully implementing the OCC strategy. Thus, the deep learning-based computer vision method was adopted in the first step of the study, and the detection performance and camera position were analyzed in an office scenario. Next, the proposed OCC strategy was used to regulate the supply air parameters and outdoor air volume in stratum ventilation based on the monitored occupant number. The traditional static control strategy was then compared to two control strategies: constant air volume and variable air volume. Occupant detection performance results showed the mean NRMSD for the five most common relative positions of the occupants and camera was 0.1109, with sitting back to camera having the lowest accuracy. Subjective response results demonstrated that, when compared to the traditional control strategy, thermal comfort was improved by 43%–73%, perceived air quality was maintained at an acceptable level, CO2 concentration was less than 700 ppm, and energy could be saved by 2.3%–8.1%. Furthermore, the lower the occupancy, the greater the improvement in comfort and the greater the energy savings. This research focused on how the stratum ventilation system responds to dynamic changes in occupancy and provided insights into reducing unnecessary energy waste while maintaining comfort.
Graphical abstract Display Omitted
Highlights Computer vision is used to detect the number of occupants in real-time. The proposed OCC strategy dynamically regulates the stratum ventilation system. The OCC-based stratum ventilation can improve thermal comfort and save energy. The CO2 concentration is lower than 700 ppm.
A novel occupant-centric stratum ventilation system using computer vision: Occupant detection, thermal comfort, air quality, and energy savings
https://orcid.org/0000-0003-4015-199X
https://orcid.org/0000-0002-5760-2147
https://orcid.org/0000-0002-2945-4685
Abstract Traditional ventilation and air conditioning systems typically operate on a predetermined schedule with fixed operating parameters. Occupant-centric control (OCC) strategies have been proposed to reduce system operation energy consumption without sacrificing thermal comfort. Indoor occupancy detection in real time is a critical step in successfully implementing the OCC strategy. Thus, the deep learning-based computer vision method was adopted in the first step of the study, and the detection performance and camera position were analyzed in an office scenario. Next, the proposed OCC strategy was used to regulate the supply air parameters and outdoor air volume in stratum ventilation based on the monitored occupant number. The traditional static control strategy was then compared to two control strategies: constant air volume and variable air volume. Occupant detection performance results showed the mean NRMSD for the five most common relative positions of the occupants and camera was 0.1109, with sitting back to camera having the lowest accuracy. Subjective response results demonstrated that, when compared to the traditional control strategy, thermal comfort was improved by 43%–73%, perceived air quality was maintained at an acceptable level, CO2 concentration was less than 700 ppm, and energy could be saved by 2.3%–8.1%. Furthermore, the lower the occupancy, the greater the improvement in comfort and the greater the energy savings. This research focused on how the stratum ventilation system responds to dynamic changes in occupancy and provided insights into reducing unnecessary energy waste while maintaining comfort.
Graphical abstract Display Omitted
Highlights Computer vision is used to detect the number of occupants in real-time. The proposed OCC strategy dynamically regulates the stratum ventilation system. The OCC-based stratum ventilation can improve thermal comfort and save energy. The CO2 concentration is lower than 700 ppm.
A novel occupant-centric stratum ventilation system using computer vision: Occupant detection, thermal comfort, air quality, and energy savings
Yang, Bin (author) / Liu, Yihang (author) / Liu, Pengju (author) / Wang, Faming (author) / Cheng, Xiaogang (author) / Lv, Zhihan (author)
Building and Environment ; 237
2023-04-18
Article (Journal)
Electronic Resource
English
Developing building-specific, occupant-centric thermal comfort models:A methodological approach
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