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.
| Original language | English |
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| Title of host publication | Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 |
| Editors | Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1558-1565 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781665439022 |
| DOIs | |
| State | Published - 2021 |
| Event | 2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: Dec 15 2021 → Dec 18 2021 |
Publication series
| Name | Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 |
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Conference
| Conference | 2021 IEEE International Conference on Big Data, Big Data 2021 |
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| Country/Territory | United States |
| City | Virtual, Online |
| Period | 12/15/21 → 12/18/21 |
Funding
The authors would furthermore like to thank the ALCF for supporting this work with compute time on the Theta supercomputer as part of the AutoBEM ALCC allocation. This research used resources of the ALCF, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Finally, the authors would like to thank Amir Roth and Madeline Salzman for their support and review of this project, and Jibonananda Sanyal for AutoSIM contributions for scalable simulation. This work was funded in part by field work proposal CEBT105 under US Department of Energy Building Technology Office Activity Number BT0305000, the Office of Electricity Activity Number TE1103000. This manuscript has been authored [in part] by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).