U.S. Department of EnergyOffice of Scientific and Technical Information
Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets
Abstract
Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.
- Authors:
-
- National Renewable Energy Laboratory
- Publication Date:
- Other Number(s):
- 40
- Research Org.:
- National Renewable Energy Laboratory - Data (NREL-DATA), Golden, CO (United States); National Renewable Energy Laboratory
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Federal Energy Management Program Office (EE-5F)
- Subject:
- 47 OTHER INSTRUMENTATION; 96 KNOWLEDGE MANAGEMENT AND PRESERVATION; Colorado; Golden; NREL; RSF; Research Support Facility; building systems; buildings; consumption; control strategy; data; electricity; energy; energy analysis; energy conservation; energy modeling; mathematics and computing research; resources; utilization
- OSTI Identifier:
- 1432675
- DOI:
- https://doi.org/10.7799/1432675
Citation Formats
Sheppy, Michael, Beach, A., and Pless, Shanti. Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets. United States: N. p., 2016.
Web. doi:10.7799/1432675.
Sheppy, Michael, Beach, A., & Pless, Shanti. Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets. United States. doi:https://doi.org/10.7799/1432675
Sheppy, Michael, Beach, A., and Pless, Shanti. 2016.
"Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets". United States. doi:https://doi.org/10.7799/1432675. https://www.osti.gov/servlets/purl/1432675. Pub date:Tue Aug 09 04:00:00 UTC 2016
@article{osti_1432675,
title = {Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets},
author = {Sheppy, Michael and Beach, A. and Pless, Shanti},
abstractNote = {Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.},
doi = {10.7799/1432675},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Aug 09 04:00:00 UTC 2016},
month = {Tue Aug 09 04:00:00 UTC 2016}
}