A Data-Driven Approach to Nation-Scale Building Energy Modeling
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.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1846526
- Resource Relation:
- Conference: 2021 IEEE International Conference on Big Data (IEEE BigData 2021) - Orlando, Florida, United States of America - 12/15/2021 5:00:00 AM-12/18/2021 5:00:00 AM
- Country of Publication:
- United States
- Language:
- English
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