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.

Original languageEnglish (US)
Title of host publicationASHRAE Transactions - ASHRAE Winter Conference
PublisherAmer. Soc. Heating, Ref. Air-Conditoning Eng. Inc.
Pages422-433
Number of pages12
Volume122
ISBN (Electronic)9781939200259
StatePublished - 2016
Event2016 ASHRAE Winter Conference - Orlando, United States
Duration: Jan 23 2016Jan 27 2016

Other

Other2016 ASHRAE Winter Conference
CountryUnited States
CityOrlando
Period1/23/161/27/16

ASJC Scopus subject areas

  • Mechanical Engineering
  • Building and Construction

Fingerprint Dive into the research topics of 'Automated data mining methods for identifying energy efficiency opportunities using whole-building electricity data'. Together they form a unique fingerprint.

  • Cite this

    Howard, P., Runger, G., Reddy, T. A., & Katipamula, S. (2016). Automated data mining methods for identifying energy efficiency opportunities using whole-building electricity data. In ASHRAE Transactions - ASHRAE Winter Conference (Vol. 122, pp. 422-433). Amer. Soc. Heating, Ref. Air-Conditoning Eng. Inc..