Longitudinal dataset of human-building interactions in U.S. offices

Langevin, Jared

doi:10.1038/s41597-019-0273-5

Title: Longitudinal dataset of human-building interactions in U.S. offices

Abstract

Adaptive interactions between building occupants and their surrounding environments affect both energy use and environmental quality, as demonstrated by a large body of modeling research that quantifies the impacts of occupant behavior on building operations. Yet, available occupant field data are insufficient to explore the mechanisms that drive this interaction. This paper introduces data from a one year study of 24 U.S. office occupants that recorded a comprehensive set of possible exogenous and endogenous drivers of personal comfort and behavior over time. The longitudinal data collection protocol merges individual thermal comfort, preference, and behavior information from online daily surveys with datalogger readings of occupants’ local thermal environments and control states, yielding 2503 survey responses alongside tens of thousands of concurrent behavior and environment measurements. These data have been used to uncover links between the built environment, personal variables, and adaptive actions, and the data contribute to international research collaborations focused on understanding the human-building interaction.

Authors:

^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Building Technology and Urban Systems Division

Publication Date:: Tue Nov 26 00:00:00 EST 2019

Research Org.:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 1624276

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: Scientific Data

Additional Journal Information:: Journal Volume: 6; Journal Issue: 1; Journal ID: ISSN 2052-4463

Publisher:: Nature Publishing Group

Country of Publication:: United States

Language:: English

Subject:: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Science & Technology - Other Topics

Citation Formats


                    Langevin, Jared. Longitudinal dataset of human-building interactions in U.S. offices.  United States: N. p., 2019. 
Web.  doi:10.1038/s41597-019-0273-5.

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                    Langevin, Jared. Longitudinal dataset of human-building interactions in U.S. offices.  United States.  https://doi.org/10.1038/s41597-019-0273-5

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                    Langevin, Jared. Tue .  
"Longitudinal dataset of human-building interactions in U.S. offices".  United States.  https://doi.org/10.1038/s41597-019-0273-5.  https://www.osti.gov/servlets/purl/1624276.

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@article{osti_1624276,

  title        = {Longitudinal dataset of human-building interactions in U.S. offices},

  author       = {Langevin, Jared},

  abstractNote = {Adaptive interactions between building occupants and their surrounding environments affect both energy use and environmental quality, as demonstrated by a large body of modeling research that quantifies the impacts of occupant behavior on building operations. Yet, available occupant field data are insufficient to explore the mechanisms that drive this interaction. This paper introduces data from a one year study of 24 U.S. office occupants that recorded a comprehensive set of possible exogenous and endogenous drivers of personal comfort and behavior over time. The longitudinal data collection protocol merges individual thermal comfort, preference, and behavior information from online daily surveys with datalogger readings of occupants’ local thermal environments and control states, yielding 2503 survey responses alongside tens of thousands of concurrent behavior and environment measurements. These data have been used to uncover links between the built environment, personal variables, and adaptive actions, and the data contribute to international research collaborations focused on understanding the human-building interaction.},

  doi          = {10.1038/s41597-019-0273-5},

  journal      = {Scientific Data},

  number       = 1,

  volume       = 6,

  place        = {United States},

  year         = {Tue Nov 26 00:00:00 EST 2019},

  month        = {Tue Nov 26 00:00:00 EST 2019}

}

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