A Mobility-Driven Approach to Modeling Building Energy

Anne Berres; Piljae Im; Kuldeep Kurte; Melissa Allen-Dumas; Gautam Thakur; Jibonananda Sanyal

doi:10.1109/BigData47090.2019.9006308

A Mobility-Driven Approach to Modeling Building Energy

Anne Berres, Piljae Im, Kuldeep Kurte, Melissa Allen-Dumas, Gautam Thakur, Jibonananda Sanyal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

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
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	https://doi.org/10.1109/BigData47090.2019.9006308
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

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
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).

Funders	Funder number
U. S. Department of Energy
US Department of Energy
UT-Battelle, LLC	DE-AC05-00OR22725
U.S. Department of Energy

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BigData47090.2019.9006308

Cite this

Berres, A., Im, P., Kurte, K., Allen-Dumas, M., Thakur, G., & Sanyal, J. (2019). A Mobility-Driven Approach to Modeling Building Energy. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 3887-3895). Article 9006308 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006308

Berres, Anne ; Im, Piljae ; Kurte, Kuldeep et al. / A Mobility-Driven Approach to Modeling Building Energy. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 3887-3895 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

@inproceedings{1bd22c4168e2441bb0e4df329e511c04,

title = "A Mobility-Driven Approach to Modeling Building Energy",

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.",

author = "Anne Berres and Piljae Im and Kuldeep Kurte and Melissa Allen-Dumas and Gautam Thakur and Jibonananda Sanyal",

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year = "2019",

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doi = "10.1109/BigData47090.2019.9006308",

language = "English",

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Berres, A, Im, P, Kurte, K, Allen-Dumas, M , Thakur, G & Sanyal, J 2019, A Mobility-Driven Approach to Modeling Building Energy. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9006308, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 3887-3895, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 12/9/19. https://doi.org/10.1109/BigData47090.2019.9006308

A Mobility-Driven Approach to Modeling Building Energy. / Berres, Anne; Im, Piljae; Kurte, Kuldeep et al.
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3887-3895 9006308 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A Mobility-Driven Approach to Modeling Building Energy

AU - Berres, Anne

AU - Im, Piljae

AU - Kurte, Kuldeep

AU - Allen-Dumas, Melissa

AU - Thakur, Gautam

AU - Sanyal, Jibonananda

PY - 2019/12

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

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A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

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A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

Berres A, Im P, Kurte K, Allen-Dumas M , Thakur G, Sanyal J. A Mobility-Driven Approach to Modeling Building Energy. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3887-3895. 9006308. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). doi: 10.1109/BigData47090.2019.9006308

A Mobility-Driven Approach to Modeling Building Energy

Abstract

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Funding

UN SDGs

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