Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

Stephen Frank, Xin Jin, Joseph Robertson, Howard Cheung, Ryan Elmore, Gregor Henze, Michael Heaney

Research output: Contribution to conferencePaper


Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.
Original languageAmerican English
Number of pages14
StatePublished - 2016
Event2016 ACEEE Summer Study on Energy Efficiency in Buildings - Pacific Grove, California
Duration: 21 Aug 201626 Aug 2016


Conference2016 ACEEE Summer Study on Energy Efficiency in Buildings
CityPacific Grove, California

Bibliographical note

Available from ACEEE: see http://aceee.org/files/proceedings/2016/data/index.htm; see NREL/CP-5500-65924 for preprint

NREL Publication Number

  • NREL/CP-5500-67077


  • building energy models
  • commercial buildings
  • fault detection and diagnosis
  • FDD


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