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A platform for research: civil engineering, architecture and urbanism
An intelligent anti-infection ventilation strategy: From occupant-centric control and computer vision perspectives
https://orcid.org/0000-0001-9136-0949
Highlights The ventilation performance based on the occupant-centric control was improved. Wells-Riley model was modified, considering occupant-related information. The occupant-related information was combined with anti-infection ventilation. The work provided a reference for occupant-centric environmental dynamic control.
Abstract Occupant-centric control of the environment carries outstanding significance in several fields. Especially in the context of epidemics, occupant-centric environmental control can contribute to the prevention and energy saving. From the perspective of anti-infection ventilation, the traditional approach is to make static corrections to the Wells-Riley (W-R) model according to the environmental characteristics to calculate a reasonable ventilation rate. However, frequent changes in the actual scenario (e.g., occupant status) can easily lead to the failure of such static ventilation in achieving its expected effects, thereby resulting in increased ventilation energy consumption and fluctuations in infection risk. In this study, an intelligent anti-infection ventilation strategy is proposed by combining computer vision with occupant-centric control. Results show that the proposed strategy can dynamically maintain the infection risk around a target value (e.g., 1%). Compared with the ventilation strategy based on a static W-R model, the proposed strategy saves 63.07% of ventilation energy consumption (saves 34.20% of energy consumption of HVAC system). A hardware platform is also built and tested to demonstrate the feasibility of the proposed strategy. This study contributes new ideas to balance the energy required for ventilation with the infection risk, and provides a reference for occupant-centric dynamic control and building occupants’ health.
An intelligent anti-infection ventilation strategy: From occupant-centric control and computer vision perspectives
https://orcid.org/0000-0001-9136-0949
Highlights The ventilation performance based on the occupant-centric control was improved. Wells-Riley model was modified, considering occupant-related information. The occupant-related information was combined with anti-infection ventilation. The work provided a reference for occupant-centric environmental dynamic control.
Abstract Occupant-centric control of the environment carries outstanding significance in several fields. Especially in the context of epidemics, occupant-centric environmental control can contribute to the prevention and energy saving. From the perspective of anti-infection ventilation, the traditional approach is to make static corrections to the Wells-Riley (W-R) model according to the environmental characteristics to calculate a reasonable ventilation rate. However, frequent changes in the actual scenario (e.g., occupant status) can easily lead to the failure of such static ventilation in achieving its expected effects, thereby resulting in increased ventilation energy consumption and fluctuations in infection risk. In this study, an intelligent anti-infection ventilation strategy is proposed by combining computer vision with occupant-centric control. Results show that the proposed strategy can dynamically maintain the infection risk around a target value (e.g., 1%). Compared with the ventilation strategy based on a static W-R model, the proposed strategy saves 63.07% of ventilation energy consumption (saves 34.20% of energy consumption of HVAC system). A hardware platform is also built and tested to demonstrate the feasibility of the proposed strategy. This study contributes new ideas to balance the energy required for ventilation with the infection risk, and provides a reference for occupant-centric dynamic control and building occupants’ health.
An intelligent anti-infection ventilation strategy: From occupant-centric control and computer vision perspectives
https://orcid.org/0000-0001-9136-0949
Highlights The ventilation performance based on the occupant-centric control was improved. Wells-Riley model was modified, considering occupant-related information. The occupant-related information was combined with anti-infection ventilation. The work provided a reference for occupant-centric environmental dynamic control.
Abstract Occupant-centric control of the environment carries outstanding significance in several fields. Especially in the context of epidemics, occupant-centric environmental control can contribute to the prevention and energy saving. From the perspective of anti-infection ventilation, the traditional approach is to make static corrections to the Wells-Riley (W-R) model according to the environmental characteristics to calculate a reasonable ventilation rate. However, frequent changes in the actual scenario (e.g., occupant status) can easily lead to the failure of such static ventilation in achieving its expected effects, thereby resulting in increased ventilation energy consumption and fluctuations in infection risk. In this study, an intelligent anti-infection ventilation strategy is proposed by combining computer vision with occupant-centric control. Results show that the proposed strategy can dynamically maintain the infection risk around a target value (e.g., 1%). Compared with the ventilation strategy based on a static W-R model, the proposed strategy saves 63.07% of ventilation energy consumption (saves 34.20% of energy consumption of HVAC system). A hardware platform is also built and tested to demonstrate the feasibility of the proposed strategy. This study contributes new ideas to balance the energy required for ventilation with the infection risk, and provides a reference for occupant-centric dynamic control and building occupants’ health.
An intelligent anti-infection ventilation strategy: From occupant-centric control and computer vision perspectives
Wang, Haorui (author) / Wang, Junqi (author) / Feng, Zhuangbo (author) / Haghighat, Fariborz (author) / Cao, Shi-Jie (author)
Energy and Buildings ; 296
2023-07-25
Article (Journal)
Electronic Resource
English