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Title: A Data-Driven Approach to Nation-Scale Building Energy Modeling

Abstract

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.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
  1. ORNL
  2. University of Tennessee, Knoxville (UTK)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1846526
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
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

Citation Formats

Berres, Andy, Bass, Brett, Adams, Mark, Garrison, Eric, and New, Joshua. A Data-Driven Approach to Nation-Scale Building Energy Modeling. United States: N. p., 2021. Web. doi:10.1109/BigData52589.2021.9671786.
Berres, Andy, Bass, Brett, Adams, Mark, Garrison, Eric, & New, Joshua. A Data-Driven Approach to Nation-Scale Building Energy Modeling. United States. https://doi.org/10.1109/BigData52589.2021.9671786
Berres, Andy, Bass, Brett, Adams, Mark, Garrison, Eric, and New, Joshua. 2021. "A Data-Driven Approach to Nation-Scale Building Energy Modeling". United States. https://doi.org/10.1109/BigData52589.2021.9671786. https://www.osti.gov/servlets/purl/1846526.
@article{osti_1846526,
title = {A Data-Driven Approach to Nation-Scale Building Energy Modeling},
author = {Berres, Andy and Bass, Brett and Adams, Mark and Garrison, Eric and New, Joshua},
abstractNote = {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.},
doi = {10.1109/BigData52589.2021.9671786},
url = {https://www.osti.gov/biblio/1846526}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {12}
}

Conference:
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