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In this paper, we present a first-time cloud-based eco-feedback and gaming platform that aims to promote energy conserving thermostat-adjustment behaviors in multi-unit residential buildings. To achieve this goal, we introduce a new modeling approach for personalized eco-feedback design integrated with a collaborative social game to assist residents enhance their thermostat use while promoting community-level energy savings. Our 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 data from the field study 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 showed the positive effect of the intervention in the thermostat-adjustment behaviors, which resulted in an increase in the indoor temperature during the cooling season compared to the baseline. Findings from the user interaction analysis and post-experiment interviews also revealed 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. • Presented a smart eco-feedback and gaming platform that aims to promote energy conserving thermostat-adjustment behaviors. • Introduced a new modeling approach for personalized eco-feedback design integrated with a collaborative social game. • Developed visual and voice user interfaces to facilitate behavioral changes in a user-centric approach. • Deployed the developed platform in a multi-unit residential community. • Conducted a field study to evaluate the effect of the platform on occupant behavior. [ABSTRACT FROM AUTHOR]

• Introduced a new paradigm for energy-aware communities. • Presented a new a sociotechnical modeling approach based on utility theory. • Inferred attributes affecting the thermostat responses of each household during an eco-feedback intervention. • Quantified the impact of the eco-feedback on households' thermostat-adjustment behavior. Heating and cooling (HC) energy use is responsible for about 42% of the total annual energy consumption of the average household in the U.S and it is significantly affected by residents' energy-related behavior. In this paper, our goal is to realize a new paradigm for energy-aware communities that leverages smart eco-feedback devices and social games to engage residents in understanding and reducing their home HC energy use. Towards this goal, we present a new sociotechnical modeling approach based on utility theory to reveal causal effects in human decision-making and infer attributes affecting the thermostat adjustment behavior of each household during an eco-feedback intervention. Our approach 1) is based on a utility model that quantifies residents' preferences over indoor temperatures given decision attributes related to their thermal environment and eco-feedback design and 2) incorporates latent parameters that determine the unique behavioral characteristics of each household. For parameter learning, we develop a hierarchical Bayesian model with non-centered parameterization calibrated using field data collected from a multi-unit residential community located in Fort Wayne, IN. The dataset comprises two parts; a baseline period without any behavioral intervention, and an intervention period, where personalized eco-feedback and social games are deployed through resident engagement devices with smart thermostat control capabilities, including a wall-mounted tablet and smart speaker. Through the model calibration, we quantify the impact of the eco-feedback on households' thermostat-adjustment behaviors. We propose that the utility model developed in this work can serve as the foundation for analyzing resident behavior in connected residential communities with eco-feedback energy-saving programs. [ABSTRACT FROM AUTHOR]