|Title||Electricity Used by Office Equipment and Network Equipment in the U.S.: Detailed Report and Appendices|
|Year of Publication||2001|
|Authors||Kaoru Kawamoto, Jonathan G Koomey, Michael K Ting, Bruce Nordman, Richard E Brown, Mary Ann Piette, Alan K Meier|
|Institution||Lawrence Berkeley National Laboratory|
|Keywords||Enduse, Energy End-Use Forecasting, EUF|
In spite of the recent explosive growth in the use of office and network equipment, there has been no recent study that estimates in detail how much electricity is consumed by that equipment in the United States. In this study, we examined energy use by office equipment and network equipment at the end of 1999. We classified office equipment into 11 types; for each type we estimated annual energy consumption for residential, commercial, and industrial use by combining estimates of stock, power requirements, usage, and saturation of power management. We also classified network equipment into six types and estimated the annual energy consumption for each type. We found that total direct power use by office and network equipment is about 74 TWh per year, which is about 2% of total electricity use in the U.S. When electricity used by telecommunications equipment and electronics manufacturing is included, that figure rises to 3% of all electricity use (Koomey 2000). More than 70% of the 74 TWh/year is dedicated to office equipment for commercial use. We also found that power management currently saves 23 TWh/year, and complete saturation and proper functioning of power management would achieve additional savings of 17 TWh/year. Furthermore, complete saturation of night shut down for equipment not required to operate at night would reduce power use by an additional 7 TWh/year. Finally, we compared our current estimate with our 1995 forecast for 1999. We found that the total difference between our current estimate and the previous forecast is less than 15% and identified the factors that led to inaccuracies in the previous forecast. We also conducted a sensitivity analysis of the uncertainties in our current forecast and identified the data sets that have the largest impact on our current estimate of energy use.
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