@inproceedings{65172f0f8436428daa5228b2bda52dbd,
title = "Automated data mining methods for identifying energy efficiency opportunities using whole-building electricity data",
abstract = "Automated detection of schedule- and operation-related energy savings opportunities in commercial buildings can help building owners lower operating expenses while also reducing adverse societal impacts such as global greenhouse gas emissions. We propose automated methods of identifying certain energy-efficiency opportunities (EEOs) in commercial buildings using only whole-building electricity consumption and local climate data. Our two-step approach uses piecewise linear regression and density-based robust regression model residual clustering to detect both schedule- and operation-related electricity consumption faults. This paper discusses results obtainedfrom applying this approach to two all-electric office buildings meant to demonstrate our model's effectiveness in identifying such EEOs. Ways by which the analysis results can be conveniently and succinctly presented to building managers and operators are also suggested.",
author = "Philip Howard and George Runger and Reddy, {T Agami} and Srinivas Katipamula",
note = "Funding Information: This research was funded by the U.S. Department of Energy through Pacific Northwest National Laboratory contract # 234021. We thank them for providing the necessary funding and much of the data used to evaluate the methodology proposed. The assistance of Saurabh Jalori during the preliminary stages of this research is also appreciated. Publisher Copyright: {\textcopyright} 2016 ASHRAE.; 2016 ASHRAE Winter Conference ; Conference date: 23-01-2016 Through 27-01-2016",
year = "2016",
language = "English (US)",
series = "ASHRAE Transactions",
publisher = "American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE)",
pages = "422--433",
booktitle = "ASHRAE Transactions",
}