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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(R) and OpenStudio(R) 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 Laboratory (NREL), Golden, CO (United States)
  2. Oak Ridge National Laboratory
  3. Purdue University
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1598973
Report Number(s):
NREL/JA-5500-76026
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Buildings
Additional Journal Information:
Journal Volume: 9; Journal Issue: 12
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: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:10.3390/buildings9120239.
@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(R) and OpenStudio(R) 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:
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This content will become publicly available on November 22, 2020
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Works referenced in this record:

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


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


Vacuum insulation panels for building application
journal, November 2005