Human decision making during eco-feedback intervention in smart and connected energy-aware communities

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 the-ory 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 attri-butes related to their thermal environment and eco-feedback design and 2) incorporates latent parame-ters 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 per-sonalized 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 behav-iors. 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. (c) 2022 Elsevier B.V. All rights reserved.