The thesis, titled Identifying Emerging Technologies and Techniques to Assess Indoor Environmental Quality and its Impact on Occupant Health, consists of an in-depth literature review outlining the various impacts of building factors on inhabitant health. Approximately 120 studies analyzing how environmental factors influence occupant health were reviewed and 25 were used to build this literature review. The thesis provides insight into the definitions of well-being, health, and the built environment and analyzes the relationship between the three. This complex relationship has been at the forefront of academic research in recent years, especially given the impact of the COVID-19 pandemic. Essentially, an individual’s health and well-being is encompassed by their physical, mental, and social state of being. Due to the increasing amount of time spent in indoor environments the built environment influences these measures of health and well-being through various environmental factors (Indoor Air Quality, humidity, temperature, lighting, acoustics, ergonomics) defining the overall Indoor Environmental Quality. This thesis reviewed the mentioned intervention and experimental studies conducted to determine how fluctuations in environmental factors influence reported health results of occupants in the short and long term. Questionnaires, interviews, medical tests, physical measurements, and sensors were used to track occupant health measures. Sensors are also used to record environmental factor levels and are now beginning to be incorporated into the building production process to promote occupant health in healthy and smart buildings. The goal is ultimately to develop these smart and healthy buildings using study results and advancing technologies and techniques as outlined in the thesis.

Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors. As a result, the initiative smart building which aims to not only manage electrical consumption in an efficient way but also reduce the damaging effect of greenhouse gases on the environment has been launched. Another important technology being promoted by government agencies is the smart grid which manages energy usage across a wide range of buildings in an effort to reduce cost and increase reliability and transparency. As a great amount of efforts have been devoted to these two initiatives by either exploring the smart grid designs or developing technologies for smart buildings, the research studying how the smart buildings and smart grid coordinate thus more efficiently use the energy is currently lacking. In this dissertation, a "system-of-system" approach is employed to develop an integrated building model which consists a number of buildings (building cluster) interacting with smart grid. The buildings can function as both energy consumption unit as well as energy generation/storage unit. Memetic Algorithm (MA) and Particle Swarm Optimization (PSO) based decision framework are developed for building operation decisions. In addition, Particle Filter (PF) is explored as a mean for fusing online sensor and meter data so adaptive decision could be made in responding to dynamic environment. The dissertation is divided into three inter-connected research components. First, an integrated building energy model including building consumption, storage, generation sub-systems for the building cluster is developed. Then a bi-level Memetic Algorithm (MA) based decentralized decision framework is developed to identify the Pareto optimal operation strategies for the building cluster. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity. Secondly, a multi-objective PSO based decision framework is developed to reduce the computational effort of the MA based decision framework without scarifying accuracy. With the improved performance, the decision time scale could be refined to make it capable for hourly operation decisions. Finally, by integrating the multi-objective PSO based decision framework with PF, an adaptive framework is developed for adaptive operation decisions for smart building cluster. The adaptive framework not only enables me to develop a high fidelity decision model but also enables the building cluster to respond to the dynamics and uncertainties inherent in the system.