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OR-16-033 -- Automated Data Mining Methods for Identifying Energy Efficiency Opportunities Using Whole-Building Electricity Data
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OR-16-033 -- Automated Data Mining Methods for Identifying Energy Efficiency Opportunities Using Whole-Building Electricity Data
Conference Proceeding by ASHRAE, 2016
Philip Howard; T. Agami Reddy, PhD, PE, Fellow ASHRAE; George Runger, PhD;Srinivas Katipamula, PhD, PE, Fellow ASHRAE
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Automated detection of schedule- and operation-relatedenergy savings opportunities in commercial buildings can helpbuilding owners lower operating expenses while also reducingadverse societal impacts such as global greenhouse gas emissions.We propose automated methods of identifying certainenergy-efficiency opportunities (EEOs) in commercial buildingsusing only whole-building electricity consumption andlocal climate data. Our two-step approach uses piecewiselinear regression and density-based robust regression modelresidual clustering to detect both schedule- and operation relatedelectricity consumption faults. This paper discussesresults obtained from applying this approach to two all-electricoffice buildings meant to demonstrate our models effectivenessin identifying such EEOs. Ways by which the analysisresults can be conveniently and succinctly presented to buildingmanagers and operators are also suggested.
Citation: 2016 Winter Conference, Orlando, FL, Transactions 2016, Vol 122 pt. 1