An Open, Cloud-Based Platform for Whole-Building Fault Detection and Diagnostics

Stephen Frank, Jason Nichols

Research output: NRELPresentation


Small commercial buildings in the U.S. waste an estimated 300 Trillion BTU (approximately $6 billion in energy costs) annually due to faults, but lack cost-effective automated fault detection and diagnosis (AFDD) tools. NREL and GE Global Research are partnering to develop hybrid AFDD algorithms tailored to the unique needs of small commercial buildings (</=10,000 sq. ft.). The approach combines physics-based energy modeling tools with state-of-the-art machine learning algorithms to enable diagnostics using modeled input data. NREL, in partnership with Purdue University, has adapted DOE's physics-based modeling software EnergyPlus and OpenStudio to simulate common building faults. Cloud-based implementations of these physics-based models were used to generate data for multiple climate zones. The data were then used to train machine learning algorithms, such as those available in GE's Predix platform, to detect and diagnose building faults. Key features of the system including the automated, cloud-implementation of EnergyPlus, genetic algorithms for automated model feature selection, multiclass detection and diagnosis models, and anomaly detection algorithms will be presented.
Original languageAmerican English
Number of pages22
StatePublished - 2018

Publication series

NamePresented at the 2018 Intelligent Building Operations Workshop, 7 July 2018, West Lafayette, Indiana

NREL Publication Number

  • NREL/PR-5500-71860


  • automated fault detection
  • buildings
  • diagnosis
  • energy modeling
  • EnergyPlus
  • machine learning
  • OpenStudio


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