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.
Citation: 2016 Winter Conference, Orlando, FL, Transactions 2016, Vol 122 pt. 1
Product Details
- Published:
- 2016
- Number of Pages:
- 12
- Units of Measure:
- Dual
- File Size:
- 1 file , 3.1 MB
- Product Code(s):
- D-OR-16-033
