Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation
by
Janghyun Kim 1,*
, Stephen Frank 1, Piljae Im 2, James E. Braun 3,4, David Goldwasser 1 and Matt Leach 1
Janghyun Kim 1,*, Stephen Frank 1, Piljae Im 2, James E. Braun 3,4, David Goldwasser 1 and Matt Leach 1
1
National Renewable Energy Laboratory, Buildings & Thermal Sciences Center, 15013 Denver West Parkway, Golden, CO 80401, USA
2
Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA
3
School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA
4
Ray W. Herrick Laboratories, Purdue University, 140 S. Martin Jischke Dr., West Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
Buildings 2019, 9(12), 239; https://doi.org/10.3390/buildings9120239
Received: 5 October 2019 / Revised: 18 November 2019 / Accepted: 20 November 2019 / Published: 22 November 2019
Automated fault detection and diagnosis (AFDD) tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, access to high-quality training data for such algorithms is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus® and OpenStudio® to generate a cost-effective training data set for developing AFDD algorithms. Part II (this paper) first presents a methodology of validating fault models with OpenStudio and then presents validation results, which are compared against measurements from a reference building. We discuss the results of our experiments with eight different faults in the reference building (a total of 39 different baseline and faulted scenarios), including our methodology for using fault models along with the reference building model to simulate the same faulted scenarios. Then, we present validation of the fault models by comparing results of simulations and experiments either quantitatively or qualitatively.
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Keywords:
automated fault detection and diagnosis; fault model; building energy modeling; EnergyPlus; OpenStudio; validation; fault experiment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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Externally hosted supplementary file 1
Link: https://openei.org/doe-opendata/dataset/curated-test-fault-data-set
Description: The curated fault experiment data set consists of tagged and fully described time series representing measured faults from the AFDD test building (ORNL’s Flexible Research Platform [FRP]), including baseline performance and faulty performance. A total of 10 different faults are tested for 49 different faulted and unfaulted scenarios with various fault intensity levels.
MDPI and ACS Style
Kim, J.; Frank, S.; Im, P.; Braun, J.E.; Goldwasser, D.; Leach, M. Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation. Buildings 2019, 9, 239.
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