An Occupant-Centric Control Strategy for Indoor Thermal Comfort, Air Quality and Energy Management

39 Pages Posted: 5 Dec 2022

See all articles by ZU WANG

ZU WANG

Guizhou University

John Kaiser Calautit

University of Nottingham

Paige Wenbin Tien

University of Nottingham

Shuangyu Wei

University of Nottingham

Wuxia Zhang

University of Nottingham

Yupeng Wu

University of Nottingham

Liang Xia

University of Nottingham, Ningbo - University of Nottingham Ningbo China

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 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: artificial intelligence, Buildings, Deep learning, Occupant-Centric control, HVAC temperature setpoint control, Thermal comfort, Indoor air quality

Suggested Citation

WANG, ZU and Calautit, John Kaiser and Tien, Paige Wenbin and Wei, Shuangyu and Zhang, Wuxia and Wu, Yupeng and Xia, Liang, An Occupant-Centric Control Strategy for Indoor Thermal Comfort, Air Quality and Energy Management. Available at SSRN: https://ssrn.com/abstract=4293660 or http://dx.doi.org/10.2139/ssrn.4293660

ZU WANG (Contact Author)

Guizhou University ( email )

Guizhou
China

John Kaiser Calautit

University of Nottingham ( email )

University Park
Nottingham, NG8 1BB
United Kingdom

Paige Wenbin Tien

University of Nottingham ( email )

University Park
Nottingham, NG8 1BB
United Kingdom

Shuangyu Wei

University of Nottingham ( email )

University Park
Nottingham, NG8 1BB
United Kingdom

Wuxia Zhang

University of Nottingham ( email )

University Park
Nottingham, NG8 1BB
United Kingdom

Yupeng Wu

University of Nottingham ( email )

University Park
Nottingham, NG8 1BB
United Kingdom

Liang Xia

University of Nottingham, Ningbo - University of Nottingham Ningbo China ( email )

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