Author: Lingzhe Wang
Date and time: June 23th, 2023, at 2:00 PM
Location: EGR-2162, DeWALT meeting
Zoom link: https://umd.zoom.us/j/2731945012
Zoom meeting ID: 273 194 5012
Committee:
Professor Jelena Srebric, Chair/Advisor
Professor Hosam Fathy
Professor Jin-Oh Hahn
Professor Bao Yang
Professor Donald Milton, Dean’s Representative
Title: Occupant-oriented Indoor Environmental Controls in Public Spaces
Abstract: The indoor environment has significant impacts on the health and comfort of building occupants. In addition, occupant behavior can affect building energy consumption. It is essential to consider actual occupant needs when controlling indoor environmental systems. To provide a healthy, comfortable, and energy-efficient indoor environment, the present dissertation presents a comprehensive research framework for occupant-oriented indoor environmental controls by conducting (i) air quality characterization in occupant breathing zone, (ii) data-driven thermal comfort identification, and (iii) simultaneous air quality, thermal comfort, and building energy controls.
For air quality characterization in occupant breathing zone, the present dissertation characterized aerosol plumes associated with the risk of airborne virus transmission to investigate the occupant requirements for air quality controls. The study considered both the aerosol plume source strength and convective transport capability by conducting experiments with 18 human subjects. The source strength was characterized by the source aerosol emission rate, and the convective transport capability was characterized by the plume influence distance. The performances of multiple mitigation strategies were tested. The findings show that controlling the air quality in the breathing zone is crucial for protecting occupants from getting infected by airborne infectious microorganisms.
For data-driven thermal comfort identification, the present dissertation developed data-driven models to predict actual occupant thermal comfort based on physiological variables. By incorporating multiple HRV indices along with wrist temperatures, the performance of the models was significantly improved, achieving more than four times the accuracy compared to models based solely on wrist temperatures. This highlights the crucial role of HRV as physiological variables in accurately predicting thermal comfort. With the F1 score, the performance evaluation index of the developed machine learning thermal comfort model, exceeded the value of 0.90, this investigation provides a reliable thermal comfort prediction method, which could be used in actual building occupant-oriented controls.
For simultaneous air quality, thermal comfort, and building energy controls, this dissertation developed a wearable micro air cleaner and deployed the extremum seeking control. The wearable micro air cleaner achieved 60% – 70% protective efficiency for both nasal and mouth breathing. Importantly, unlike current mitigation methods such as masks, this device allows users to be thermal comfortable when the indoor air temperature is above 25 °C. Additionally, this dissertation implemented the extremum seeking control to balance the trade-offs between individual thermal comfort preferences and building energy consumption in real-time. This control method successfully achieved energy savings of up to 22% compared to a constant temperature setpoint of 24 °C. The developed framework for simultaneous air quality, thermal comfort, and building energy controls holds great potential in providing building occupants with a healthy, comfortable, and energy-efficient indoor environment.