Lawrence Berkeley National Lab Building 59 (in EN)
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.
- Authors:
-
- Lawrence Berkeley National Laboratory; Lawrence Berkeley National Laboratory
- Lawrence Berkeley National Laboratory
- Publication Date:
- 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:
- 1762808
- DOI:
- https://doi.org/10.25584/LBNLBLDG59/1762808
Citation Formats
Luo, Na, and Hong, Tianzhen. Lawrence Berkeley National Lab Building 59. United States: N. p., 2021.
Web. doi:10.25584/LBNLBLDG59/1762808.
Luo, Na, & Hong, Tianzhen. Lawrence Berkeley National Lab Building 59. United States. doi:https://doi.org/10.25584/LBNLBLDG59/1762808
Luo, Na, and Hong, Tianzhen. 2021.
"Lawrence Berkeley National Lab Building 59". United States. doi:https://doi.org/10.25584/LBNLBLDG59/1762808. https://www.osti.gov/servlets/purl/1762808. Pub date:Tue Jan 26 23:00:00 EST 2021
@article{osti_1762808,
title = {Lawrence Berkeley National Lab Building 59},
author = {Luo, Na and Hong, Tianzhen},
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.},
doi = {10.25584/LBNLBLDG59/1762808},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 26 23:00:00 EST 2021},
month = {Tue Jan 26 23:00:00 EST 2021}
}
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