In developed nations, 20-40% of greenhouse gas emissions and more than one-third of energy consumption are attributable to buildings. Among various available strategies, the building sector has the greatest potential for carbon emission reduction. Leadership in Energy and Environmental Design (LEED) took early action to promote sustainable designs in buildings and has become the most well-known rating system in the field of building sustainability. However, little research has evaluated the effects of LEED on occupant pro-environmental behavior. To examine this, a Post-Occupancy Evaluation (POE) was conducted in a traditional, green featured, and LEED certified building case to compare the similarities and differences in environmental awareness, perceptions, and perceived ease or difficulty of pro-environmental behaviors, as well as to assess the degree to which pro-environmental behaviors were exhibited by occupants. This was used to determine if the aforementioned factors influence occupant behavior in different building cases. Ease or difficulty of pro-environmental behaviors and environmental awareness were found to be significant factors in influencing pro-environmental behavior in the LEED certified and green featured building cases. In addition, being in a LEED building appears to influence occupant pro-environmental behavior in a positive way. Also, there is evidence to suggest that being in a green featured building appears to influence occupants to exhibit pro-environmental behavior as well. These findings are valuable for owners and designers that want occupants in their buildings to exhibit pro-environmental behavior. / Master of Science

Buildings account for 40% of energy consumption in the US. Despite all improvements in buildings shell, equipment, and design, CO2 emissions from buildings are increasing as a result of increased energy consumption. Since occupants spend more than 90% of their time indoors, they are inseparable and significant elements of building system dynamics. Hence, there is a great potential for energy efficiency in buildings using a wide range of programs such as education, intervention, energy feedback, etc. Due to advancement of technology and accessibility of high resolution energy consumption data, utility companies are enabled to focus on implementing energy feedback programs to induce energy efficiency and reduce the peak energy load in the commercial and residential sector. In order to better understand various aspects of energy feedback programs, in the first chapter of this dissertation, I conduct a comprehensive literature review on the state-of-the-art energy feedback study methods and identify gaps of knowledge and challenges faced by researchers in the field. Accordingly, the future research vision is laid out at the intersection of methods and gaps of knowledge used in energy feedback studies and future research opportunities and questions are provided. One of the major gaps of knowledge I identified in the literature review is the lack of quantitative analyses used to investigate the variability of occupant responses to commercial buildings energy feedback programs to evaluate the need for targeted and tailored energy feedback programs. In the second chapter, I conducted a comprehensive analysis on occupant energy-use responses under the influence of a uniform energy feedback program. Furthermore, I investigated the effectiveness of notifications on increasing the level of engagement of the occupants in these studies. The results supported the existence of a variability in responses and engagement level in a uniform energy feedback program which may be due to intra-class variability of occupant behavior. In the third chapter, based on the established need for a targeted energy feedback program, I investigate the predictability of occupant energy consumption behavior and its correlation with energy consumption. The results report that 46% of occupants may be good candidates for targeted energy feedback programs due to their combination of higher levels of energy-use and predictability of their energy consumption behavior. / Ph. D.

Plug loads—traditionally viewed as behaviorally motivated and beyond the control of designers and operations—are now seen as an integral part of achieving low-energy building targets. Higher education institutions are increasingly recognizing the environmental impacts of campus facilities through holistic approaches to energy savings including energy efficient design and occupant engagement. Residence halls are a compelling example because students bring large numbers of electronics to their rooms and have unlimited access to power for an all-inclusive room rate and resource usage competitions and campaigns are commonplace. However, limited research exists on residence halls plug loads. This dissertation asked the following of residence halls: (1) What are the measured plug loads and how do they compare with design estimates? (2) What role do building design characteristics play in plug loads? (3) What are the specific occupant behaviors that could influence future design? (4) How can plug loads be better understood in terms of behavior, design, and operations? To answer these questions, a sequential mixed methods study included field measurements and student surveys in six residence halls on three Oregon campuses followed by 24 interviews with designers, operators, and students. Findings suggest that plug loads in occupied residence halls are higher and usage profiles differ from design predictions. Results do not show significant correlations between design characteristics and plug loads but suggest that some room/suite level features may play a somewhat stronger role. Survey responses indicated that students are doing more with fewer smart devices, which suggests opportunities for students sharing energy intensive devices. Lighting emerged as both a practical and a social consideration. Finally, the data revealed “balance of power” as a coherent process that explicates the relationships between design, operations, and behavior. Designers have the power to recommend plug load strategies and technologies but are limited by costs, maintenance, and political concerns; operations personnel have the power to impose limits on student power usage but are often reluctant to interfere with the overall living experience; and students have the power to use plug load electricity with few restrictions. This suggests that the balance may be skewed toward student behavior.

<p>    </p> <p>Technological advances and gaming have assisted users in becoming energy-efficient or raising awareness about energy efficiency. However, these games typically take place in schools and workplaces. Low-income households, which spend a larger percentage of income on utilities compared to average income households, exhibit greater sensitivity to energy disturbances. Despite this, there has been limited research on applying these technologies in low-income households. </p> <p><br></p> <p>The dissertation addresses the research gap concerning motivating low-income households to adopt new technologies focused on implementing energy-efficient HVAC behaviors. To achieve this objective, a gamification approach is employed, integrating a competitive social game into a cloud-based application named MySmartE. This application offers personalized eco-feedback and enables voice commands using Amazon Alexa. The game is deployed in two multi-residential low-income household communities located in Indiana. The collected data from field studies is analyzed to explore various aspects, including community interactions during the gaming seasons, technology adoption, and factors influencing participation in the social game. The findings reveal a positive correlation between increased gaming interac- tions and the adoption of MySmartE technology within these communities, underscoring the potential of gamification and technology to effectively engage low-income households in adopting energy-efficient practices. </p>

<p>  </p> <p>Heating and cooling (HC) energy use account for about 40% of the total annual energy consumption and cost of an average household in the U.S and it is significantly affected by residents’ energy-related behavior. This is particularly important for low-income residents in the U.S. who spend a larger portion of their income (i.e., about 16%) on home energy costs compared to average-income households (i.e., 4%). To address opportunities for reducing residential HC energy usage without requiring physical building upgrades, this thesis presents a new paradigm for smart and connected energy-aware communities that leverage smart eco-feedback devices and social games to engage residents in understanding and reducing their home energy use.</p> <p><br></p> <p>First, this Thesis presents a new modeling approach for personalized eco-feedback design integrated with a collaborative social game to assist residents to enhance their thermostat use while promoting community-level energy savings. The modeling framework is integrated into a cloud-based application, MySmartE, with visual (wall-mounted tablet) and voice (Alexa) user interfaces to facilitate behavioral changes in a user-centric approach. The platform is deployed in a multi-unit residential community in Fort Wayne, IN and the experimental data are used to investigate: (i) how occupants’ thermostat behaviors changed after using the MySmartE app; (ii) how users interacted with the app during the game; and (iii) how was users’ experience with the developed platform. Despite the heterogeneous characteristics of households, the results from the field study show the positive effect of the intervention in the thermostat-adjustment behaviors, which results in an increase in the indoor temperature during the cooling season compared to the baseline period. Findings from the user interaction analysis and post-experiment interviews also reveal the significant potential to nudge households’ energy conservation behaviors with the developed platform along with the challenges that should be tackled to derive long-term behavior changes. </p> <p><br></p> <p>Second, this Thesis introduces a sociotechnical modeling approach based on utility theory to reveal causal effects in human decision-making and infer attributes affecting households’ thermostat responses during an eco-feedback intervention. This modeling approach (i) is based on a utility model that quantifies residents’ preferences over indoor temperatures given decision attributes related to their thermal environment and eco-feedback and (ii) incorporates latent parameters that are inferred to determine the unique behavioral characteristics of each household. For parameter learning, a hierarchical Bayesian model is developed with a non-centered parameterization and calibrated to the field data. Based on the calibration results, the proposed model quantifies the impact of the eco-feedback on households’ thermostat-adjustment behaviors and serves as a foundation for analyzing resident behavior in connected residential communities with eco-feedback energy-saving programs.</p> <p><br></p> <p>Finally, this Thesis presents a modeling approach for investigating the decision trends of residents in goal-oriented collaborative social games while considering their decision preferences and goal achievement capabilities. The proposed approach involves a mechanism design method that derives optimal decisions by conducting counterfactual simulations given various scenarios of goal and reward sets. This modeling approach (i) re-defines utility functions to include decision attributes that reflect user preferences on the game status; (ii) calibrates the model to learn the decision preferences of the residents; (iii) simulates the decision-making process of residents by solving the Nash Equilibrium for a given set of game scenarios. The results revealed the decision trends of the residents given the various goals and rewards along with the potential goal achievement trends and the resulting variations in the marginal community utility.</p>

The goal of this dissertation is to demonstrate that data from a remotely located building can be utilized for energy modeling of a similar type of building and to demonstrate how to use this remote data without physically moving the data from one server to another using Virtual Information Fabric Infrastructure (VIFI). In order to achieve this goal, firstly an EnergyPlus model was created for Greek Life Center, a campus building located at University of North Texas campus at Denton in Texas, USA. Three thermal comfort models of Fanger model, Pierce two-node model and KSU two-node model were compared in order to find which one of these three models is most accurate to predict occupant thermal comfort. This study shows that Fanger's model is most accurate in predicting thermal comfort. Secondly, an experimental data pertaining to lighting usage and occupancy in a single-occupancy office from Carnegie Mellon University (CMU) has been implemented in order to perform energy analysis of Greek Life Center assuming that occupants in this building's offices behave similarly as occupants in CMU. Thirdly, different data types, data formats and data sources were identified which are required in order to develop a city-scale urban building energy model (CS-UBEM). Two workflows were created, one for an individual scale building energy model and another one for CS-UBEM. A new innovative infrastructure called as Virtual Information Fabric Infrastructure (VIFI) has been introduced in this dissertation. The workflows proposed in this study will demonstrate in the future work that by using VIFI infrastructure to develop building energy models there is a potential of using data for remote servers without actually moving the data. It has been successfully demonstrated in this dissertation that data located at remote location can be used credibly to predict energy consumption of a newly built building. When the remote experimental data of both lighting and occupancy are implemented, 4.57% energy savings was achieved in the Greek Life Center energy model.