Doctoral Dissertations

Orcid ID

Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Data Science and Engineering

Major Professor

Joshua New

Committee Members

Joshua New, Russell Zaretzki, Audris Mockus, Piljae Im


Energy consumption is steadily increasing year over year in the United States (US). Climate change and anthropogenically forced shifts in weather have a significant impact on energy use as well as the resilience of the built environment and the electric grid. With buildings accounting for about 40% of total energy use in the US, building energy modeling (BEM) at a large scale is critical. This work advances that effort in a number of ways. First, current BEM approaches, their ability to scale to large geographical areas, and global climate models are reviewed. Next, a methodology for large-scale BEM is illustrated, displaying its capability to create a digital twin of a utility service area consisting of more than 178,000 electrical meters in and around Chattanooga, Tennessee. This urban BEM (UBEM) framework is unique in its ability to scale beyond localized tax assessor data, which can be a limiting factor in the size of UBEM analyses. A partnership was formed with a Chattanooga electrical utility to use real 15-minute electricity data to assign building parameters and empirically validate the models. Several analyses were performed on the buildings in the service area, including simulating several building technologies and climate change resilience. After the utility-scale analysis, the scope was broadened to the entire US. A method was created by which climate models can be used to project building energy use for all commercial buildings in the US through 2100 using a floor-area scaling technique. US building energy climate research to this point has either been localized to individual building types in specific regions of the country or has evaluated energy use across the US as a whole. With simulated error rates of less than 4% compared to commercial building energy survey data, this bottom-up method can be used to effectively forecast building energy related to climate change. The utility scale UBEM framework was also expanded to model every building in the US individually. A modeling effort of this size has never been done on an individual building basis (more than 125 million buildings). The methodology can show that US nation-scale analyses can be accomplished using high-performance computing (HPC) resources and can be used as a baseline for UBEM researchers in the future while the models can be used for simulation-informed analysis across the country.

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