Abstract
Buildings are one of the primary energy consumers in any city's energy use [12]. Presence and absence of humans is a major contributing factor to the energy use in a building. In this paper, we present an approach to generating a realistic model of human building occupancy throughout a typical work week. We use the Toolbox for Urban Mobility Systems (TUMS) to generate a synthetic population based on population distribution estimates, we schedule the population's daily commute based on National Household Travel Survey (NHTS) survey data, and we simulate their daily travel patterns using an agent-based transportation simulation (TRANSIMS). We process and fuse the simulation output to produce a list of the first and last seen location of each agent in the simulation. Based on the arrival at the last destination, we map each agent to one of the nearby buildings. Using these agent arrivals, as well as NHTS data, we create an hourly occupancy schedule for each building. We successfully demonstrate this workflow at the example of the Chicago Loop, a major business district in Chicago, Illinois.
| Original language | English |
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| Title of host publication | Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 |
| Editors | Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3887-3895 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781728108582 |
| DOIs | |
| State | Published - Dec 2019 |
| Event | 2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: Dec 9 2019 → Dec 12 2019 |
Publication series
| Name | Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 |
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Conference
| Conference | 2019 IEEE International Conference on Big Data, Big Data 2019 |
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| Country/Territory | United States |
| City | Los Angeles |
| Period | 12/9/19 → 12/12/19 |
Funding
ACKNOWLEDGMENTS The authors would like to than the Exascale Computing Project (ECP) and the U. S. Department of Energy (DOE) for funding this work. We would furthermore like to thank the ORNL Leadership Computing Facility (OLCF) for supporting this work with compute time on the Titan supercomputer. Moreover, we would like to thank Cheng Liu on his guidance on using TUMS, Srinath Ravulaparthy for his suggestion to use NHTS data, and Steve Peterson for his help with QGIS. Finally, we would like to thank IDOT for providing us with detailed traffic data in addition to their publicly available annual reports [24]. 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).