Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development
Abstract
Small commercial buildings (those with less than approximately 1000 m2 of total floor area) often do not have access to cost-effective automated fault detection and diagnosis (AFDD) tools for maintaining efficient building operations. AFDD tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, such algorithms require access to high-quality training data that 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 I (this paper) presents a library of fault models, including detailed descriptions of each fault model structure and their implementation with EnergyPlus. This paper also discusses a case study of training data set generation, representing an actual building.
- Authors:
-
[1];
[1];
[1]
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Purdue Univ., West Lafayette, IN (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- OSTI Identifier:
- 1580571
- Report Number(s):
- NREL/JA-5500-75713
Journal ID: ISSN 2075-5309
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Buildings
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 11; 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; data-driven AFDD; fault model; building energy modeling; EnergyPlus; OpenStudio
Citation Formats
Kim, Janghyun, Frank, Stephen M., Braun, James E., and Goldwasser, David. Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development. United States: N. p., 2019.
Web. doi:10.3390/buildings9110233.
Kim, Janghyun, Frank, Stephen M., Braun, James E., & Goldwasser, David. Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development. United States. https://doi.org/10.3390/buildings9110233
Kim, Janghyun, Frank, Stephen M., Braun, James E., and Goldwasser, David. Thu .
"Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development". United States. https://doi.org/10.3390/buildings9110233. https://www.osti.gov/servlets/purl/1580571.
@article{osti_1580571,
title = {Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development},
author = {Kim, Janghyun and Frank, Stephen M. and Braun, James E. and Goldwasser, David},
abstractNote = {Small commercial buildings (those with less than approximately 1000 m2 of total floor area) often do not have access to cost-effective automated fault detection and diagnosis (AFDD) tools for maintaining efficient building operations. AFDD tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, such algorithms require access to high-quality training data that 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 I (this paper) presents a library of fault models, including detailed descriptions of each fault model structure and their implementation with EnergyPlus. This paper also discusses a case study of training data set generation, representing an actual building.},
doi = {10.3390/buildings9110233},
journal = {Buildings},
number = 11,
volume = 9,
place = {United States},
year = {Thu Nov 14 00:00:00 EST 2019},
month = {Thu Nov 14 00:00:00 EST 2019}
}
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Works referencing / citing this record:
Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation
journal, November 2019
- Kim, Janghyun; Frank, Stephen; Im, Piljae
- Buildings, Vol. 9, Issue 12