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 model’s effectivenessin identifying such EEOs. Ways by which the analysisresults can be conveniently and succinctly presented to buildingmanagers and operators are also suggested.