This report discusses miscellaneous electrical loads, which are building loads that are not related to general lighting, heating, ventilation, cooling, and water heating, and typically do not provide comfort to the occupants. MELs in commercial buildings account for almost 5% of U.S. primary energy consumption.
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This publication details the design, implementation strategies, and continuous performance monitoring of NREL's Research Support Facility data center.
This case study details the design and operations of the National Renewable Energy Laboratory (NREL) Research Support Facility data center and its contributions to energy efficiency.
Conventional information technology (IT) equipment and data center spaces can consume more than 100 times the energy of standard office spaces, so the potential for energy savings is huge. You can use this application guide to reduce your equipment energy consumption in any building with a data center, server closets, or other IT equipment (computers, printers, etc.). Some of these strategies are most effective at the beginning of the design process; others can be implemented at any time and be sequenced as part of the normal procurement and replacement schedule.
Plug and process loads in commercial buildings account for 5% of U.S. primary energy consumption. Minimizing these loads is a primary challenge in the design and operation of an energy-efficient building.
This presentation describes how the designers, owners, and occupants can take advantage of opportunities to reduce plug loads in the Research Support Facility.
This presentation discusses the importance of selecting a project delivery method that balances performance, best value, and cost savings.
The Research Support Facility complex (RSF, RSF II, parking garage, and associated site lighting) was designed to produce more on-site renewable energy than it uses over the course of a typical weather year, when accounted for at the site. To date, the end use performance monitoring and verification suggests that when the RSF complex is fully built out, we will meet the annual energy use goals. Continued performance monitoring and occupant education are required to ensure annual energy use goals will continue to be met.
Empirical techniques for characterizing electrical energy use now play a key role in reducing electricity consumption, particularly miscellaneous electrical loads, in buildings. Identifying device operating modes (mode extraction) creates a better understanding of both device and system behaviors. Using clustering to extract operating modes from electrical load data can provide valuable insights into device behavior and identify opportunities for energy savings. We present a fast and effective heuristic clustering method to identify and extract operating modes in electrical load data.
This case study describes National Renewable Energy Laboratory efforts design a world-class, energy-efficient data center to support the operations of a new office building. These efforts resulted in a highly efficient data center that demonstrated considerable energy savings in its first 11 months of operations. Using legacy data center performance as a baseline, the new facility cut energy use by nearly 1.45 million kWh, delivering cost savings of approximately $82,000.