A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An occupant-centric control strategy for indoor thermal comfort, air quality and energy management
https://orcid.org/0000-0001-6141-1047
https://orcid.org/0000-0002-1053-1652
https://orcid.org/0000-0003-4610-203X
Graphical abstract Display Omitted
Highlights Real-time identification of occupancy and window opening using AI-powered cameras. Occupant-centric control strategy for better thermal comfort, energy efficiency and air quality. The heating energy consumption was reduced by between 0.6 % and 29.0 %. The level of indoor thermal comfort increased by up to 58.8 %. Indoor CO2 concentrations below 1000 ppm for 89.2 % of the time being occupied.
Abstract 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.
An occupant-centric control strategy for indoor thermal comfort, air quality and energy management
https://orcid.org/0000-0001-6141-1047
https://orcid.org/0000-0002-1053-1652
https://orcid.org/0000-0003-4610-203X
Graphical abstract Display Omitted
Highlights Real-time identification of occupancy and window opening using AI-powered cameras. Occupant-centric control strategy for better thermal comfort, energy efficiency and air quality. The heating energy consumption was reduced by between 0.6 % and 29.0 %. The level of indoor thermal comfort increased by up to 58.8 %. Indoor CO2 concentrations below 1000 ppm for 89.2 % of the time being occupied.
Abstract 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.
An occupant-centric control strategy for indoor thermal comfort, air quality and energy management
https://orcid.org/0000-0001-6141-1047
https://orcid.org/0000-0002-1053-1652
https://orcid.org/0000-0003-4610-203X
Graphical abstract Display Omitted
Highlights Real-time identification of occupancy and window opening using AI-powered cameras. Occupant-centric control strategy for better thermal comfort, energy efficiency and air quality. The heating energy consumption was reduced by between 0.6 % and 29.0 %. The level of indoor thermal comfort increased by up to 58.8 %. Indoor CO2 concentrations below 1000 ppm for 89.2 % of the time being occupied.
Abstract 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.
An occupant-centric control strategy for indoor thermal comfort, air quality and energy management
Wang, Zu (author) / Calautit, John (author) / Tien, Paige Wenbin (author) / Wei, Shuangyu (author) / Zhang, Wuxia (author) / Wu, Yupeng (author) / Xia, Liang (author)
Energy and Buildings ; 285
2023-02-12
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2018
| British Library Online Contents | 2018