Motivating and Quantifying Energy Efficient Behavior among Commercial Building Occupants

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Virginia Tech

The environmental and economic consequences of climate change are severe and are being exacerbated by increased global carbon emissions. In the United States, buildings account for over 40% of all domestic and 7.4% of all global CO2 emissions and therefore represent an important target for energy conservation initiatives. Even marginal energy savings across all buildings could have a profound effect on carbon emission mitigation. In order to realize the full potential of energy savings in the building sector, it is essential to maximize the energy efficiency of both buildings and the behavior of occupants who occupy them. In this vein, systems that collect and communicate building energy-use information to occupants (i.e. eco-feedback systems) have been demonstrated to motivate building occupants to significantly reduce overall building energy consumption. Furthermore, advancements in building sensor technologies and data processing capabilities have enabled the development of advanced eco-feedback systems that also allow building occupants to share energy-use data with one another and to collectively act to reduce energy consumption. In addition to monitoring building occupant energy-use, these systems are capable of collecting data about specific conservation actions taken by occupants and their interactions with different features of the eco-feedback system. However, despite recent advancements in eco-feedback and building sensor technologies, very few systems have been specifically designed to enable research on the effectiveness of different behavior-based energy conservation strategies in commercial buildings. Consequently, very little research has been conducted on how access to such systems impacts the energy-use behavior of building occupants. In this dissertation, I describe how my research over the past three years has advanced an understanding of how eco-feedback systems can impact the energy-use behavior of commercial building occupants. First, I present a novel eco-feedback system that I developed to connect building occupants over energy-use data and empower them to conserve energy while also collecting data that enables controlled studies to quantify the impacts of a wide variety of energy conservation strategies. Next, I present a commercial building study in which this eco-feedback system was used to investigate the effects of organizational network dynamics on the energy-use of individuals. I then introduce a new set of metrics based on individual energy-use data that enables the classification of individuals and building occupant networks based on their energy-use efficiency and predictability. I describe the principles behind the construction of these metrics and demonstrate how these quantitative measures can be used to increase the efficacy of behavior-based conservation campaigns by enabling targeted interventions. I conclude the dissertation with a discussion about the limitations of my research and the new research avenues that it has enabled.

Energy Efficiency, Buildings, Organizational Networks, Classification, Energy Management, Energy Monitoring