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Title: Building fault detection data to aid diagnostic algorithm creation and performance testing

Abstract

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.

Authors:
 [1];  [1];  [1];  [2]; ORCiD logo [3]
  1. Lawrence Berkeley National Laboratory
  2. Oak Ridge National Laboratory
  3. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1603785
Report Number(s):
[PNNL-SA-150821]
Grant/Contract Number:  
[AC05-76RL01830]
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Data
Additional Journal Information:
[ Journal Volume: 7; Journal Issue: 1]
Country of Publication:
United States
Language:
English

Citation Formats

Granderson, J, Lin, Guanjing, Harding, Ari, Im, Piljae, and Chen, Yan. Building fault detection data to aid diagnostic algorithm creation and performance testing. United States: N. p., 2020. Web. doi:10.1038/s41597-020-0398-6.
Granderson, J, Lin, Guanjing, Harding, Ari, Im, Piljae, & Chen, Yan. Building fault detection data to aid diagnostic algorithm creation and performance testing. United States. doi:10.1038/s41597-020-0398-6.
Granderson, J, Lin, Guanjing, Harding, Ari, Im, Piljae, and Chen, Yan. Mon . "Building fault detection data to aid diagnostic algorithm creation and performance testing". United States. doi:10.1038/s41597-020-0398-6.
@article{osti_1603785,
title = {Building fault detection data to aid diagnostic algorithm creation and performance testing},
author = {Granderson, J and Lin, Guanjing and Harding, Ari and Im, Piljae and Chen, Yan},
abstractNote = {It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.},
doi = {10.1038/s41597-020-0398-6},
journal = {Scientific Data},
number = [1],
volume = [7],
place = {United States},
year = {2020},
month = {2}
}

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Works referenced in this record:

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


The Building Data Genome Project: An open, public data set from non-residential building electrical meters
journal, September 2017


Modelica Buildings library
journal, March 2013

  • Wetter, Michael; Zuo, Wangda; Nouidui, Thierry S.
  • Journal of Building Performance Simulation, Vol. 7, Issue 4
  • DOI: 10.1080/19401493.2013.765506

EnergyPlus: creating a new-generation building energy simulation program
journal, April 2001


Multiyear microgrid data from a research building in Tsukuba, Japan
journal, February 2019

  • Vink, Karina; Ankyu, Eriko; Koyama, Michihisa
  • Scientific Data, Vol. 6, Issue 1
  • DOI: 10.1038/sdata.2019.20

Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance
journal, January 2020


Efficient and robust optimization for building energy simulation
journal, June 2016


I-BLEND, a campus-scale commercial and residential buildings electrical energy dataset
journal, February 2019

  • Rashid, Haroon; Singh, Pushpendra; Singh, Amarjeet
  • Scientific Data, Vol. 6, Issue 1
  • DOI: 10.1038/sdata.2019.15

Building analytics and monitoring-based commissioning: industry practice, costs, and savings
journal, May 2019


A performance evaluation framework for building fault detection and diagnosis algorithms
journal, June 2019