title: Occupant-centric building control : balancing occupant comfort and energy efficiency
Creators: name: Park, June Young
Publisher: name: The University of Texas at Austin
Year: 2020
Subjects: Occupant-centric controls
Building systems
Occupant behavior
Occupancy detection
Reinforcement learning

description: Building systems, i.e. heating, ventilation, and air-conditioning (HVAC) and lighting, were developed to provide a comfortable environment. As a consequence, their energy demand contributes to 30 — 40% of the total energy consumption of buildings. One way to reduce this energy consumption is through optimal operation. Technological development sparked the concept of smart buildings — automating and optimizing the operation of building systems. Unfortunately, such developments have largely focused on energy efficiency rather than on the human comfort perspective. Due to the immaturity of human-building interactions, the building industry still conventionally relies on naive assumptions about occupants, which often leads to occupant dissatisfaction and even more energy waste. Occupant-Centric Building Control (OCC) is a viable solution to mitigate the challenges related to human comfort in building controls. In the OCC approach, the control agent acquires two types of data. The first type characterizes the physical, indoor environment—illuminance, temperature, humidity, and carbon dioxide (CO2) — and its relationship with occupant satisfaction and productivity. The second type considers the interaction between the occupant and building systems — presence, light switches, window blinds, and thermostat usage — and it is indicative of the comfort or, more often, the discomfort of the occupant. By collecting these two, the control agent can adapt to unique occupant behaviors and indoor environments. It calculates the adaptive and personalized control actions, i.e., modifying set-points, moving shades or switching lights, to balance between occupant comfort and energy efficiency of building systems. In this dissertation, I studied occupancy-based and occupant behavior-based OCCs using both simulation and experimental approaches. For the occupancy-based controls, I developed an opportunistic occupancy detection (OOD) system, which analyzes occupants’ mobile device signals to infer the number of occupants in a building. The proposed OOD system is further improved by implementing the capture and recapture method (CRC). In ecology, CRC is an established estimation framework which uses the relationship found between two separate observations of the same population. Results indicated that the OOD with CRC method estimates the ratio between occupants and their mobile devices very accurately when at least 30% whole population have discoverable mobile devices. The extracted knowledge is useful for occupancy-based control, e.g., demand driven ventilation. For occupant behavior-based controls, I focused on Reinforcement Learning (RL) based OCCs for lighting (LightLearn) and thermostat (HVACLearn) controls. RL is a machine learning technique in which the learning agent interacts with its environment and uses feedback from the environment to determine the best possible action given the current state. Due to its interactive learning characteristic, RLOCC is suitable to identify the complex dynamics of human-building interactions. For example, LightLearn and HVACLearn both monitor how people use their light switch and thermostat with associated indoor environment measurements. Then the algorithms calculate adaptive and personalized set-points to operate the lighting and HVAC systems. Results showed that RL-OCC learns about occupants and indoor environments adaptively and balance between occupant comfort and energy efficiency successfully when compared to conventional control strategies. To conclude, my Ph.D. studies on OCC are prime examples of smart building systems where the control agent interacts with the occupant, understands the unique environment, and actuates building systems efficiently. As humans spend the majority of their time indoors, buildings have to ensure sufficient comfort is provided while reducing environmental impacts. Therefore, OCC will contribute to a sustainable and comfortable transformation of the building stock, leading to smart buildings and cities