Demonstration of Data Center Energy Use Prediction Software
Date Published |
09/2013
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Publication Type | Report
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LBL Report Number |
LBNL-6608E
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Abstract |
This report documents a demonstration of a software modeling tool from Romonet that was used to predict energy use and forecast energy use improvements in an operating data center. The demonstration was conducted in a conventional data center with a 15,500 square foot raised floor and an IT equipment load of 332 kilowatts. It was cooled using traditional computer room air handlers and a compressor-based chilled water system. The data center also utilized an uninterruptible power supply system for power conditioning and backup. Electrical energy monitoring was available at a number of locations within the data center. The software modeling tool predicted the energy use of the data center's cooling and electrical power distribution systems, as well as electrical energy use and heat removal for the site. The actual energy used by the computer equipment was recorded from power distribution devices located at each computer equipment row. The model simulated the total energy use in the data center and supporting infrastructure and predicted energy use at energy-consuming points throughout the power distribution system. The initial predicted power levels were compared to actual meter readings and were found to be within approximately 10 percent at a particular measurement point, resulting in a site overall variance of 4.7 percent. Some variances were investigated, and more accurate information was entered into the model. In this case the overall variance was reduced to approximately 1.2 percent. The model was then used to predict energy use for various modification opportunities to the data center in successive iterations. These included increasing the IT equipment load, adding computer room air handler fan speed controls, and adding a water-side economizer. The demonstration showed that the software can be used to simulate data center energy use and create a model that is useful for investigating energy efficiency design changes. |
Year of Publication |
2013
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