Enabling Thermally Adaptive and Sustainable Built Environments through Sensing and Modeling of Human-Building Interactions

Fundamental interactions between buildings and their occupants have a multitude of significant impacts. First, built environments critically affect occupants’ health and wellness, especially given that people spend more than 90% of time indoors. Among several environmental factors, the lack of thermal comfort is a common problem despite nearly half of the building energy being consumed by heating, ventilation, and air conditioning (HVAC) systems. Humans, in turn, closely influence the sustainable operation of buildings through various occupant energy-use behaviors. Recent studies indicate that actions performed or abstained by occupants have a major influence on building energy performance and can negate the benefits of investing in energy-efficient building systems. This dissertation focused on these two primary interplays of human-building interactions. First, uncertainties in occupants’ thermal comfort due to the varying human physiological, psychological, and behavioral factors lead to significant thermal dissatisfaction and often result in sick building syndrome. A potential solution is the human-in-the-loop approach to sense thermal comfort and provide more personalized environments. However, existing comfort assessing approaches have several key limitations including the need for continuous human input to adjust setpoints, lack of actionable human data in comfort prediction, intrusiveness and privacy concerns, and difficulty in integrating within HVAC operations. To address these issues, this research first investigated the integration of environmental data with human bio-signals collected from wristbands and smartphones for thermal comfort prediction and achieved 85% classification accuracy. This approach however required humans to provide their information from wearable devices and respond to a polling app. To address these limitations, the research further explored low-cost infrared thermal camera networks to non-intrusively collect facial skin temperature for real-time comfort assessment in both single and multi-occupancy spaces. Similar prediction accuracy is achieved without using any personal devices. Building on these comfort sensing approaches, this dissertation demonstrates how to bridge personal comfort models and physiological predictive models to determine optimum setpoints for improved overall satisfaction or reduced energy use while maintaining comfort. The proposed sensing and optimization methods can serve as a basis for automated environment control to improve human experience and well-being. The second part of this research addressed why behavior interventions result in different energy reduction rates and identified two important gaps: lack of fundamental understanding of behavioral determinants of occupants, and lack of methods to quantitatively describe the varying occupant characteristics which affect the effectiveness of interventions. To address these gaps, the research developed a conceptual framework which explains occupant behaviors with three determining factors - motivation, opportunity, and ability (MOA) incorporating insights from building science and social psychology. Based on MOA levels, clustering analysis and agent-based modeling were applied to classify occupancy characteristics and evaluate the effectiveness of a chosen intervention. The framework was improved by integrating MOA factors with two classical behavioral theories to address the challenges in defining and measuring MOA factors. The results showed an improved explanatory power over a single theory and suggested that favorable behaviors can be promoted by motivating occupants, removing environmental constraints, and improving occupants’ abilities. This framework enables decision-makers to develop effective and economical interventions to solicit behavioral change and achieve building efficiency. Building upon these two perspectives of human-building interactions, future studies can investigate how personalized thermal environments will improve occupant behaviors in interacting with HVAC systems and the corresponding impacts on building energy consumption.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153410/1/dliseren_1.pd