CAREER: Holistic Assessment of the Impacts of Connected Buildings and People on Community Energy Planning and Management

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Future smart communities will consist of connected buildings, distributed energy resources, electric vehicles, battery energy storage systems, and peer-to-peer energy trading at the individual building level. These features will require an optimal and distributed coordination of a cluster of buildings, distributed energy resources, and a smart grid, while considering human behavior and mobility. The development and engagement of smart meters and social media have opened a new paradigm for community energy planning and underscore the need for a holistic engineering framework to model a new energy infrastructure of a community. This project aims to bridge this gap between smart building technologies, social interactions, energy trading and grid operation through an integrated research and education program.In the project, an approach will be developed that advances current state-of-the-art community energy planning and management technology through use of large-scale heterogeneous data sets, including smart meter, occupant behavior and social media data, an urban scale energy and behavior co-simulation platform, and an advanced integrated building, grid and distributed energy resources control framework. The research aims to will bridge the gap between data science and smart building technologies. To address the challenges involved, the following research tasks will be pursued: (1) Develop a new mathematical framework to learn community connections. Occupant behavior from heterogeneous data sets will be learned, classifies and clustered. A new Markov modeling of occupant movement and presence will be developed; (2) Develop an innovative co-simulation platform to integrate a new occupant behavior model with community level energy modeling. A new spatiotemporal occupant model linking presence and behavior will be developed, and physical models of community urban energy infrastructure will be integrated and coupled with a new behavior model; and (3) Develop a hierarchical control framework to evaluate and quantify energy savings from individual buildings to a whole community based on community connections and an integrated co-simulation platform. A multilayer model with predictive control architecture will be designed to address the time-discrepancy issuesof various energy systems. This approach sets it sights on a systematic understanding of how a set of diverse occupant behavior impacts operation decisions and energy efficiency as a whole in a future smart city. If successful, the approach may transform current sustainable community design and operation to actively involve macro-level occupant behavior and human mobility modeling. The PI will work with existing minority engineering programs, student organizations, and honors programs at UTSA designed to recruit and graduate minority students, such as the NSF Louis Stokes Alliances for Minority Participation (LSAMP), the Ronald McNair Scholars programs, and the Society of Hispanic Professional Engineers. The principal investigator will also maintain a website to make the research results and online games available to the public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.