DeepVision based detection for energy-efficiency and indoor air quality enhancement in highly polluted spaces - Wei, Shuangyu; Tien, Paige; Zhang, Wuxia; Wei, Zhichen; Wang, Zu; Calautit, John Kaiser (Journal of Building Engineering, 2024)
Cooking can generate substantial heat from cooking equipment, potentially resulting in reduced thermal comfort levels if this excess heat is not adequately dissipated. Additionally, it can significantly affect indoor air quality (IAQ), not only in kitchen spaces but also in adjacent areas lacking sufficient ventilation. To ensure a healthy and comfortable indoor environment while avoiding unnecessary energy use, this research proposes an approach for detecting equipment usage. Utilizing deep learning and computer vision techniques (referred to as DeepVision), this method aids the operation of demand-driven ventilation systems in highly polluted spaces. A Faster RCNN model was employed and trained to detect kitchen equipment usage in real-time. The ventilation rate for the kitchen space was adjusted based on this real-time detection. Experimental tests were carried out in a case study kitchen and results showed that the detection model achieved an overall F1 score of 0.9142. Overall, the model achieved good performance in real-time detection, accurately identifying appliances in use, such as stoves, ovens, and toasters. Field experimental results showed the advantages of combining mechanical ventilation methods, such as extractor fans and cooker hoods, to mitigate IAQ issues while also achieving energy savings. Moreover, the energy simulations demonstrate its potential to reduce energy consumption by dynamically adjusting ventilation rates in response to real-time equipment usage. When the fan speed was varied according to the real-time detection method, the PM2.5 concentration in the cooking period was 16.5 % lower, with only a 3.7 % rise in fan power and a 0.8 % rise in daily heating demand, compared to using a constant fan speed.
Keywords: _important; _z:class:countries; _z:class:themes; oa:sdg:7 Affordable and clean energy; oa:topic:T10121 Building Energy Efficiency and Thermal Comfort Optimization; oa:topic:T10990 Airborne Transmission of Respiratory Viruses; oa:topic:T12120 Low-Cost Air Quality Monitoring Systems; openalex:n:1; openalex:yes; selected_for:Predictive models for classroom conditions 2024

DeepVision based detection for energy-efficiency and indoor air quality enhancement in highly polluted spaces - Wei, Shuangyu; Tien, Paige; Zhang, Wuxia; Wei, Zhichen; Wang, Zu; Calautit, John Kaiser (Journal of Building Engineering, 2024)
Cooking can generate substantial heat from cooking equipment, potentially resulting in reduced thermal comfort levels if this excess heat is not adequately dissipated. Additionally, it can significantly affect indoor air quality (IAQ), not only in kitchen spaces but also in adjacent areas lacking sufficient ventilation. To ensure a healthy and comfortable indoor environment while avoiding unnecessary energy use, this research proposes an approach for detecting equipment usage. Utilizing deep learning and computer vision techniques (referred to as DeepVision), this method aids the operation of demand-driven ventilation systems in highly polluted spaces. A Faster RCNN model was employed and trained to detect kitchen equipment usage in real-time. The ventilation rate for the kitchen space was adjusted based on this real-time detection. Experimental tests were carried out in a case study kitchen and results showed that the detection model achieved an overall F1 score of 0.9142. Overall, the model achieved good performance in real-time detection, accurately identifying appliances in use, such as stoves, ovens, and toasters. Field experimental results showed the advantages of combining mechanical ventilation methods, such as extractor fans and cooker hoods, to mitigate IAQ issues while also achieving energy savings. Moreover, the energy simulations demonstrate its potential to reduce energy consumption by dynamically adjusting ventilation rates in response to real-time equipment usage. When the fan speed was varied according to the real-time detection method, the PM2.5 concentration in the cooking period was 16.5 % lower, with only a 3.7 % rise in fan power and a 0.8 % rise in daily heating demand, compared to using a constant fan speed.
Keywords: _important; _z:class:countries; _z:class:themes; oa:sdg:7 Affordable and clean energy; oa:topic:T10121 Building Energy Efficiency and Thermal Comfort Optimization; oa:topic:T10990 Airborne Transmission of Respiratory Viruses; oa:topic:T12120 Low-Cost Air Quality Monitoring Systems; openalex:n:1; openalex:yes; selected_for:Predictive models for classroom conditions 2024

An occupant-centric control strategy for indoor thermal comfort, air quality and energy management - Wang, Zu; Calautit, John Kaiser; Tien, Paige; Wei, Shuangyu; Zhang, Wuxia; Wu, Yupeng; Xia, Liang (Energy and Buildings, 2023)
Recently, Occupant-Centric Control (OCC) strategies have gained mounting interest. Previous studies made use of OCC strategies for adjusting the operation of heating/cooling systems, improving indoor thermal comfort and governing mechanical ventilation systems. However, a very limited number of studies have applied OCC strategies to natural ventilation systems. Further, the feasibility of establishing OCC strategies for controlling indoor thermal comfort, energy use and specifically air quality has received much less attention and investigation. This paper presented an Occupant-Centric Heating and Natural Ventilation Control (OCHNVC) strategy for enhancing indoor thermal comfort, building energy performance and indoor air quality. Firstly, real-time profiles of occupant behavior and window opening in a case study building were collected using artificial intelligence (AI)-powered cameras and deep vision algorithms. Secondly, shallow artificial-neural-networks predictive models were established for forecasting the responses of the studied building to different levels of occupant behavior and window opening behavior. Thirdly, an OCHNVC strategy tailored to the studied room was proposed and applied to the studied room. The strategy could lower heating energy consumption by between 0.6 % and 29.0 % and improve the level of indoor thermal comfort by between 0 % and 58.8 %, relative to a conventional control strategy. Moreover, the conventional window control strategy only maintained indoor CO2 concentrations below 1000 ppm for 59.7 % of the period that occupants were within the studied room, while the proposed controller could do so for 89.2 % of the period. Future works shall focus on experimentally deploying the strategy to real buildings and evaluating its performance.
Keywords: _important; _z:class:countries; _z:class:themes; _z:no_pdf; oa:keyword:indoor thermal comfort; oa:sdg:7 Affordable and clean energy; oa:topic:T10121 Building Energy Efficiency and Thermal Comfort Optimization; oa:topic:T10766 Urban Heat Islands and Mitigation Strategies; oa:topic:T11371 Urban Wind Environment and Air Quality Modeling; openalex:n:1; openalex:yes; selected_for:Predictive models for classroom conditions 2024

An Occupant-Centric Control Strategy for Indoor Thermal Comfort, Air Quality and Energy Management - WANG, ZU; Calautit, John Kaiser; Tien, Paige; Wei, Shuangyu; Zhang, Wuxia; Wu, Yupeng; Xia, Liang (SSRN Electronic Journal, 2022)
Keywords: _z:class:countries; _z:class:themes; _z:no_pdf; _z:not_duplicate; oa:keyword:indoor thermal comfort; oa:sdg:7 Affordable and clean energy; oa:topic:T10121 Building Energy Efficiency and Thermal Comfort Optimization; openalex:n:1; openalex:yes

An Occupant-Centric Control Strategy for Indoor Thermal Comfort, Air Quality and Energy Management - Wang, Zu; Calautit, John Kaiser; Tien, Paige Wenbin; Wei, Shuangyu; Zhang, Wuxia; Wu, Yupeng; Xia, Liang (2022)
Recently, Occupant-Centric Control (OCC) strategies have gained mounting interest. Previous studies made use of OCC strategies for adjusting the operation of heating/cooling systems, improving indoor thermal comfort and governing mechanical ventilation systems. However, a very limited number of studies have applied OCC strategies to natural ventilation systems. Further, the feasibility of establishing OCC strategies for controlling indoor thermal comfort, energy use and specifically air quality has received much less attention and investigation. This paper presented an Occupant-Centric Heating and Natural Ventilation Control (OCHNVC) strategy for enhancing indoor thermal comfort, building energy performance and indoor air quality. Firstly, real-time profiles of occupant behavior and window opening in a case study building were collected using artificial intelligence (AI)-powered cameras and deep vision algorithms. Secondly, shallow artificial-neural-networks predictive models were established for forecasting the responses of the studied building to different levels of occupant behavior and window opening behavior. Thirdly, an OCHNVC strategy tailored to the studied room was proposed and applied to the studied room. The strategy could lower heating energy consumption by between 0.6% and 29.0% and improve the level of indoor thermal comfort by between 0% and 58.8%, relative to a conventional control strategy. Moreover, the conventional window control strategy only maintained indoor CO2 concentrations below 1,000 ppm for 59.7% of the period that occupants were within the studied room, while the proposed controller could do so for 89.2% of the period. Future works shall focus on experimentally deploying the strategy to real buildings and evaluating its performance.
Keywords: Buildings; Deep learning; HVAC temperature setpoint control; Indoor air quality; Occupant-Centric control; Thermal comfort; _z:class:countries; _z:class:themes; _z:not_duplicate; artificial intelligence; oa:keyword:indoor thermal comfort; oa:sdg:7 Affordable and clean energy; oa:topic:T10121 Building Energy Efficiency and Thermal Comfort Optimization; openalex:n:1; openalex:yes

An occupant-centric control strategy for indoor thermal comfort, air quality and energy management - Wang, Zu; Calautit, John Kaiser; Tien, Paige; Wei, Shuangyu; Zhang, Wuxia; Wu, Yupeng; Xia, Liang (Energy and Buildings, 2023)
Recently, Occupant-Centric Control (OCC) strategies have gained mounting interest. Previous studies made use of OCC strategies for adjusting the operation of heating/cooling systems, improving indoor thermal comfort and governing mechanical ventilation systems. However, a very limited number of studies have applied OCC strategies to natural ventilation systems. Further, the feasibility of establishing OCC strategies for controlling indoor thermal comfort, energy use and specifically air quality has received much less attention and investigation. This paper presented an Occupant-Centric Heating and Natural Ventilation Control (OCHNVC) strategy for enhancing indoor thermal comfort, building energy performance and indoor air quality. Firstly, real-time profiles of occupant behavior and window opening in a case study building were collected using artificial intelligence (AI)-powered cameras and deep vision algorithms. Secondly, shallow artificial-neural-networks predictive models were established for forecasting the responses of the studied building to different levels of occupant behavior and window opening behavior. Thirdly, an OCHNVC strategy tailored to the studied room was proposed and applied to the studied room. The strategy could lower heating energy consumption by between 0.6 % and 29.0 % and improve the level of indoor thermal comfort by between 0 % and 58.8 %, relative to a conventional control strategy. Moreover, the conventional window control strategy only maintained indoor CO2 concentrations below 1000 ppm for 59.7 % of the period that occupants were within the studied room, while the proposed controller could do so for 89.2 % of the period. Future works shall focus on experimentally deploying the strategy to real buildings and evaluating its performance.
Keywords: _important; _z:class:countries; _z:class:themes; _z:no_pdf; oa:keyword:indoor thermal comfort; oa:sdg:7 Affordable and clean energy; oa:topic:T10121 Building Energy Efficiency and Thermal Comfort Optimization; oa:topic:T10766 Urban Heat Islands and Mitigation Strategies; oa:topic:T11371 Urban Wind Environment and Air Quality Modeling; openalex:n:1; openalex:yes; selected_for:Predictive models for classroom conditions 2024

An Occupant-Centric Control Strategy for Indoor Thermal Comfort, Air Quality and Energy Management - Wang, Zu; Calautit, John Kaiser; Tien, Paige Wenbin; Wei, Shuangyu; Zhang, Wuxia; Wu, Yupeng; Xia, Liang (2022)
Recently, Occupant-Centric Control (OCC) strategies have gained mounting interest. Previous studies made use of OCC strategies for adjusting the operation of heating/cooling systems, improving indoor thermal comfort and governing mechanical ventilation systems. However, a very limited number of studies have applied OCC strategies to natural ventilation systems. Further, the feasibility of establishing OCC strategies for controlling indoor thermal comfort, energy use and specifically air quality has received much less attention and investigation. This paper presented an Occupant-Centric Heating and Natural Ventilation Control (OCHNVC) strategy for enhancing indoor thermal comfort, building energy performance and indoor air quality. Firstly, real-time profiles of occupant behavior and window opening in a case study building were collected using artificial intelligence (AI)-powered cameras and deep vision algorithms. Secondly, shallow artificial-neural-networks predictive models were established for forecasting the responses of the studied building to different levels of occupant behavior and window opening behavior. Thirdly, an OCHNVC strategy tailored to the studied room was proposed and applied to the studied room. The strategy could lower heating energy consumption by between 0.6% and 29.0% and improve the level of indoor thermal comfort by between 0% and 58.8%, relative to a conventional control strategy. Moreover, the conventional window control strategy only maintained indoor CO2 concentrations below 1,000 ppm for 59.7% of the period that occupants were within the studied room, while the proposed controller could do so for 89.2% of the period. Future works shall focus on experimentally deploying the strategy to real buildings and evaluating its performance.
Keywords: Buildings; Deep learning; HVAC temperature setpoint control; Indoor air quality; Occupant-Centric control; Thermal comfort; _z:class:countries; _z:class:themes; _z:not_duplicate; artificial intelligence; oa:keyword:indoor thermal comfort; oa:sdg:7 Affordable and clean energy; oa:topic:T10121 Building Energy Efficiency and Thermal Comfort Optimization; openalex:n:1; openalex:yes