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T. Agami Reddy, Ph.D., P.E., The Design School/The School of Sustainable Engineering and the Built Environment
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Phillip Howard, Arizona State University
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George Runger, Ph.D., Arizona State University
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Srinivas Katipamula, Ph.D., P.E., Pacific Northwest National Laboratory
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Automated detection of schedule- and operation-related energy savings opportunities in commercial buildings can help building owners lower operating expenses while also reducing the adverse societal impacts such as global greenhouse gas emissions. We propose automated methods of identifying certain energy efficiency opportunities in commercial buildings using only whole-building electricity consumption and local climate data. Our two-step approach uses piecewise linear regression and density-based clustering to detect both schedule- and operation-related electricity consumption faults. This paper discusses results obtained from applying this approach to two office buildings and two residential buildings meant to demonstrate our model’s effectiveness in identifying such energy efficiency opportunities. Ways by which the analysis results can be conveniently and succinctly presented to building managers and operators are also suggested.
