Friday, April 30, 2010
DISSERTATION DEFENSE PRESENTATION - Joon Ho Choi
10 May, 3:00PM, MMCH 108Bio-Sensing Building Mechanical System Controls for Sustainably Enhancing Individual Thermal Comfort
Current existing thermal control systems are operated based on thermal comfort models generated by regression formulas averaging the thermal responses over data collected during extensive experiments involving panels of human subjects. Control models developed from these studies rely on statistical regression and therefore are designed to satisfy the average, "typical" person. These models may not be appropriate for an individual whose physiological characteristics happen to be located outside of the main
stream of the distribution modeled from the experimental sample of occupants. By necessity, existing automatic control systems disregard individual characteristics such as health status, age, gender, lifestyle, ethnic or cultural origin, body mass, etc., which may affect physiological responses. Thereby these systems have serious limitations in ensuring individual thermal satisfaction.
While there have been many efforts to overcome the limitations of current technology and to improve individualized control, they are still based on pre-set programmable parameters and require physical access to a controllers. In addition, most of the attempts to make smart controllers for buildings have dealt primarily with optimizing mechanical building components to deliver uniform conditions, largely ignoring whether a generated thermal environment by building systems meet actual users' comfort and satisfaction. Over-cooling and over-heating are common unnecessary results.
Thermal control innovations for building mechanical systems are critically needed to demonstrate that meeting the physiological needs of occupants can actually save energy and improve environmental quality while enhancing user satisfaction.
The thermoregulation of the human body has a biological mechanism, homeostasis, which enables it to maintain a stable and constant body temperature by changing physiological signals including skin temperatures and heart rate. These signal patterns have the potential to provide information about each individual's current thermal perception.
The goal of this project is to establish an adaptive thermal comfort control driven by ongoing human physiological responses or bio-signals. Confirming the optimum driver of skin temperature, and location of sensors, the sensor-controller will be ergonomically and economically refined, and machine learning algorithms will be incorporated in the wireless configuration to support the optimum control of HVAC terminal units. Since the system will use machine learning component, the controller will predict thermal sensation based on the recorded database of bio-signals and indoor/outdoor climate conditions. This prediction principle will be used also for multi-occupancy space control to maximize thermal comfort for all the space occupants while minimizing energy use for heating or cooling.
Based on existing thermostatic controllers, the new bio-sensing controller will be applicable in a wide variety of new and existing buildings. The use of the intelligent wearable (or remote) sensor-controller will be demonstrated in a number of HVAC configurations. Bio-sensing controllers offer major opportunities for office, healthcare and residential buildings, especially where occupants are very sensitive to indoor environmental quality, where environmental quality and control can be linked to productivity and health, and where energy savings are critical. The bio-sensing building mechanical system control research would substantially improve occupant comfort, health, and well-being while advancing environmental sustainability with energy savings, at a small first cost for existing or new buildings as well as other built environments such as auto vehicle.