Advanced building energy management system demonstration for Department of Defense buildings

Zheng O'Neill, Trevor Bailey, Bing Dong, Madhusudana Shashanka, Dong Luo

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


This paper presents an advanced building energy management system (aBEMS) that employs advanced methods of whole-building performance monitoring combined with statistical methods of learning and data analysis to enable identification of both gradual and discrete performance erosion and faults. This system assimilated data collected from multiple sources, including blueprints, reduced-order models (ROM) and measurements, and employed advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to facilities managers. A demonstration of aBEMS was conducted in buildings at Naval Station Great Lakes. The facility building management systems were extended to incorporate the energy diagnostics and analysis algorithms, producing systematic identification of more efficient operation strategies. At Naval Station Great Lakes, greater than 20% savings were demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy-efficient operation strategies. The paper concludes with recommendations for widespread aBEMS success.

Original languageEnglish (US)
Pages (from-to)44-53
Number of pages10
JournalAnnals of the New York Academy of Sciences
Issue number1
StatePublished - Aug 2013
Externally publishedYes


  • Building energy performance
  • Energy diagnostics
  • Energy performance visualization
  • Reduced-order model

ASJC Scopus subject areas

  • General Neuroscience
  • General Biochemistry, Genetics and Molecular Biology
  • History and Philosophy of Science


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