Lawrence Berkley National Laboratory Building 59 (Raw)

Luo, Na

doi:10.25584/1763770

Title: Lawrence Berkley National Laboratory Building 59 (Raw) (in EN)

Dataset
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Abstract

The building management system in Building 59 is monitoring and archiving building-level electricity usage, HVAC and lighting system states (e.g., setpoint, temperature, flow rate, pressure), indoor environmental conditions (air temperature, relative humidity, CO2), on-site weather (air temperature, relative humidity), and especially occupant counts as well as other metrics such as Wi-Fi signal. This dataset could support multiple use cases, such as model predictive control and occupant related demand management. Raw data.

Authors:

Luo, Na

Lawrence Berkeley National Laboratory; LBNL

Publication Date:: Sun Jan 31 23:00:00 EST 2021

Other Number(s):: 69035

DOE Contract Number:: AC02-05CH11231

Research Org.:: Pacific Northwest National Laboratory 2; LBNL

Sponsoring Org.:: EE-5B

Subject:: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Indoor environmental; Occupant; Outdoor environmental; Systems and equipment operational

OSTI Identifier:: 1763770

DOI:: https://doi.org/10.25584/1763770

Citation Formats


                    Luo, Na. Lawrence Berkley National Laboratory Building 59 (Raw).  United States: N. p., 2021. 
        Web.  doi:10.25584/1763770.

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                    Luo, Na. Lawrence Berkley National Laboratory Building 59 (Raw).  United States.  doi:https://doi.org/10.25584/1763770

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                    Luo, Na. 2021.  
"Lawrence Berkley National Laboratory Building 59 (Raw)".  United States.  doi:https://doi.org/10.25584/1763770.  https://www.osti.gov/servlets/purl/1763770. Pub date:Sun Jan 31 23:00:00 EST 2021

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@article{osti_1763770,

  title        = {Lawrence Berkley National Laboratory Building 59 (Raw)},

  author       = {Luo, Na},

  abstractNote = {The building management system in Building 59 is monitoring and archiving building-level electricity usage, HVAC and lighting system states (e.g., setpoint, temperature, flow rate, pressure), indoor environmental conditions (air temperature, relative humidity, CO2), on-site weather (air temperature, relative humidity), and especially occupant counts as well as other metrics such as Wi-Fi signal. This dataset could support multiple use cases, such as model predictive control and occupant related demand management. Raw data.},

  doi          = {10.25584/1763770},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sun Jan 31 23:00:00 EST 2021},

  month        = {Sun Jan 31 23:00:00 EST 2021}

}

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DOI: https://doi.org/10.25584/1763770

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