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A Data-Driven Approach to Nation-Scale Building Energy Modeling...

by Anne S Berres, Brett C Bass, Mark B Adams, Eric Garrison, Joshua R New
Publication Type
Conference Paper
Journal Name
IEEE BigData 2021
Book Title
IEEE Big Data 2021
Publication Date
Page Numbers
1558 to 1565
Conference Name
2021 IEEE International Conference on Big Data (IEEE BigData 2021)
Conference Location
Orlando, Florida, United States of America
Conference Sponsor
Conference Date

In 2019, 125 million U.S. residential and commercial buildings consumed $412 billion in energy bills. These buildings currently consume 40% of the nation's primary energy, 73% of electricity, 80% of energy during peak electric grid use, and responsible for 39% of greenhouse gas emissions [14]. Urban-scale building energy modeling has grown significantly in the past decade, allowing individual campuses or communities of buildings to be modeled, simulated, and cost-effective solutions for intelligent management to be identified and implemented. While traditionally limited to individual counties and usually less than 2,000 buildings, the Automatic Building Energy Modeling (AutoBEM) soft-ware suite has been developed to process unconventional, nation-scale data sources to generate unique OpenStudio and EnergyPlus models of each building. Through the use of High Performance Computing (HPC) resources, every U.S. building has been simulated. This paper showcases the data layout, node partitioning, algorithmic approaches, and analytic results that were used to create, share, and analyze 124.4 million U.S. building models.