skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation

Abstract

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.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2];  [3]; ORCiD logo [1]; ORCiD logo [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Purdue Univ., West Lafayette, IN (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1598973
Alternate Identifier(s):
OSTI ID: 1606781
Report Number(s):
NREL/JA-5500-76026
Journal ID: ISSN 2075-5309
Grant/Contract Number:  
AC36-08GO28308; AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Buildings
Additional Journal Information:
Journal Volume: 9; Journal Issue: 12; Journal ID: ISSN 2075-5309
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; automated fault detection and diagnosis; fault model; building energy modeling; EnergyPlus; OpenStudio; validation; fault experiment

Citation Formats

Kim, Janghyun, Frank, Stephen M., Im, Piljae, Braun, James E., Goldwasser, David, and Leach, Matthew M. Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation. United States: N. p., 2019. Web. doi:10.3390/buildings9120239.
Kim, Janghyun, Frank, Stephen M., Im, Piljae, Braun, James E., Goldwasser, David, & Leach, Matthew M. Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation. United States. doi:https://doi.org/10.3390/buildings9120239
Kim, Janghyun, Frank, Stephen M., Im, Piljae, Braun, James E., Goldwasser, David, and Leach, Matthew M. Fri . "Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation". United States. doi:https://doi.org/10.3390/buildings9120239. https://www.osti.gov/servlets/purl/1598973.
@article{osti_1598973,
title = {Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation},
author = {Kim, Janghyun and Frank, Stephen M. and Im, Piljae and Braun, James E. and Goldwasser, David and Leach, Matthew M.},
abstractNote = {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.},
doi = {10.3390/buildings9120239},
journal = {Buildings},
number = 12,
volume = 9,
place = {United States},
year = {2019},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Save / Share:

Works referenced in this record:

A review of fault detection and diagnostics methods for building systems
journal, April 2017


Prediction of air-side particulate fouling of HVAC&R heat exchangers
journal, July 2016


Effect of the distribution of faults and operating conditions on AFDD performance evaluations
journal, August 2016


Transient pattern analysis for fault detection and diagnosis of HVAC systems
journal, November 2005


Evaporator Air-Side Fouling: Effect on Performance of Room Air Conditioners and Impact on Indoor Air Quality
journal, March 2008


The impact of fouling on the condenser of a vapor compression refrigeration system: An experimental observation
journal, February 2014


Improved methodologies for simulating unitary air conditioners at off-design conditions
journal, November 2009


The effect of improper refrigerant charging on the performance of an air conditioner with capillary tube expansion
journal, January 1990


Predicting particle deposition on HVAC heat exchangers
journal, December 2003


Analysis of HVAC system oversizing in commercial buildings through field measurements
journal, February 2014


Impacts of fouling and cleaning on the performance of plate fin and spine fin heat exchangers
journal, November 2003

  • Pak, Bock Choon; Baek, Byung Joon; Groll, Eckhard A.
  • KSME International Journal, Vol. 17, Issue 11
  • DOI: 10.1007/BF02983611

Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development
journal, November 2019


Measured effect of airflow and refrigerant charge on the seasonal performance of an air-source heat pump using R-410A
journal, July 2011


Air leakage measurement and analysis in duct systems
journal, March 2006


Smart building creation in large scale HVAC environments through automated fault detection and diagnosis
journal, March 2018


Vacuum insulation panels for building application
journal, November 2005


The Sensitivity of Chiller Performance to Common Faults
journal, July 2001


Evaluation of the impacts of refrigerant charge on air conditioner and heat pump performance
journal, November 2012


Continuous measurements of air change rates in an occupied house for 1 year: The effect of temperature, wind, fans, and windows
journal, June 2002

  • Wallace, L. A.; Emmerich, S. J.; Howard-Reed, C.
  • Journal of Exposure Science & Environmental Epidemiology, Vol. 12, Issue 4
  • DOI: 10.1038/sj.jea.7500229

Performance of a residential heat pump operating in the cooling mode with single faults imposed
journal, March 2009


Fault Detection and Diagnostics for Commercial Coolers and Freezers
journal, January 2009


Application of pattern matching method for detecting faults in air handling unit system
journal, July 2014