This project aims to develop a new paradigm for smart and connected residential communities that engages inhabitants in understanding and reducing their home energy use while increasing their environmental awareness, responsiveness to collective goals, and improving their quality of life. The research will lead to discoveries concerning how individuals, groups, and residential communities make decisions related to their home energy consumption. Based on this knowledge, this project will develop feedback mechanisms integrated into user-interactive smart devices to enable optimal energy management. The Indiana Housing and Community Development Agency, several industry stakeholders and various community action groups will be engaged in this work throughout the lifetime of the project. Smart and connected (S&C) technology will be implemented in several hundred households in multiple residential communities that will be used as research test-beds and will cover a wide range of demographics, locations, and construction. The research outcomes will be integrated in teaching modules that support curriculum and workforce development as well as capacity-building in engineering, social and economic science, and polytechnic schools at Purdue. Through sociotechnical research advances, community engagement, and dissemination, this project will create a national model for "S&C energy-aware residential communities" in the housing sector, and by example point the broader research community toward S&CC research frontiers that enhance community functioning and national prosperity.
Fundamental advances in machine learning and mechanism design, along with integrative research in human-machine interaction, behavioral and social sciences, and building energy systems will lead to discoveries that challenge our current understanding of behavior and response to feedback both at the individual and community-level. Given the large population size and the range of learning and community-based feedback mechanisms along with audio/visual end-user systems, the findings pertaining to the use of customized feedback and S&C technology to influence behavior will lead to general principles of human behavior that can be transferred to domains beyond energy use. The researchers will establish a new sociotechnical modeling approach based on Bayesian multi-scale clustering algorithms and game-theoretic models that will impact multiple disciplines and research activities in different S&CC application domains, such as energy, water, transportation, economic development, environmental quality, and urban planning.
