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.
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The General Service Administration's (GSA) Green Proving Ground (GPG) program worked with a team from the National Renewable Energy Laboratory (NREL) to identify buildings with office setups and equipment distributions typical of the wider GSA building stock. Eight buildings from GSA’s Mid-Atlantic Region, where plug loads average 21%, were selected. In each building, approximately 12 standard power strips with no control capability (the incumbent technology) were replaced with APSs, which monitored and provided power to an array of devices. More than 295 devices were monitored during the study, which consisted of three separate test periods, each four weeks in length. All buildings selected had workstation power management in place.