Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation

Zhang, Liang; Leach, Matt

doi:10.1007/s12273-021-0833-4

Title: Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation

Journal Article · Wed Aug 25 00:00:00 EDT 2021 · Building Simulation

DOI:https://doi.org/10.1007/s12273-021-0833-4· OSTI ID:1820573

^[1];

^[1]

National Renewable Energy Lab. (NREL), Golden, CO (United States)

The performance of data-driven fault detection and diagnostics (FDD) is heavily dependent on sensors. However, sensor inaccuracy and sensor faults are pervasive in building operation: inaccurate and missing sensor readings deteriorate FDD performance; sensor inaccuracy will also affect the selection of sensor for data-driven FDD in the model training process, which is another key factor of data-driven FDD performance. Sensor accuracy and sensor selection individually are well-studied research topics in this field, but the impact of sensor accuracy on sensor selection and its further impact on FDD performance has not been evaluated and quantified. In this paper, we developed a novel analysis methodology that comprehensively evaluates sensor fault on sensor selection and FDD accuracy. Monte Carlo simulation is applied to deal with multiple stochastic sensor inaccuracy and provide probabilistic analysis results of the impact of sensor inaccuracy on sensor selection and FDD accuracy. This methodology focuses on the net impact of fault states across a full sensor set. The developed methodology can be used for the early-stage sensor design and operation-stage sensor maintenance. Furthermore, a case study is conducted to demonstrate the analysis methodology using a commercial building model crated to Flexible Research Platform located at Oak Ridge National Laboratory, USA.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1820573

Report Number(s):: NREL/JA-5500-80670; MainId:77454; UUID:7d586f35-6bf8-4bb2-a301-b4b70c38a4e7; MainAdminID:60609

Journal Information:: Building Simulation, Vol. 15, Issue 5; ISSN 1996-3599

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

References (17)

A random forest guided tour Biau, Gérard; Scornet, Erwan TEST, Vol. 25, Issue 2 https://doi.org/10.1007/s11749-016-0481-7	journal	April 2016
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Fault detection, diagnosis and data recovery for a real building heating/cooling billing system Chen, Youming; Lan, Lili Energy Conversion and Management, Vol. 51, Issue 5 https://doi.org/10.1016/j.enconman.2009.12.004	journal	May 2010
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis Dai, Xuewu; Gao, Zhiwei IEEE Transactions on Industrial Informatics, Vol. 9, Issue 4 https://doi.org/10.1109/TII.2013.2243743	journal	November 2013
Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis Du, Zhimin; Fan, Bo; Jin, Xinqiao Building and Environment, Vol. 73 https://doi.org/10.1016/j.buildenv.2013.11.021	journal	March 2014
Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning Han, H.; Gu, B.; Wang, T. International Journal of Refrigeration, Vol. 34, Issue 2 https://doi.org/10.1016/j.ijrefrig.2010.08.011	journal	March 2011
Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I Katipamula, Srinivas; Brambley, Michael HVAC&R Research, Vol. 11, Issue 1 https://doi.org/10.1080/10789669.2005.10391123	journal	January 2005
Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development Kim, Janghyun; Frank, Stephen; Braun, James E. Buildings, Vol. 9, Issue 11 https://doi.org/10.3390/buildings9110233	journal	November 2019
Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation Kim, Janghyun; Frank, Stephen; Im, Piljae Buildings, Vol. 9, Issue 12 https://doi.org/10.3390/buildings9120239	journal	November 2019
Fault detection and diagnosis for building cooling system with a tree-structured learning method Li, Dan; Zhou, Yuxun; Hu, Guoqiang Energy and Buildings, Vol. 127 https://doi.org/10.1016/j.enbuild.2016.06.017	journal	September 2016
Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance Lin, Guanjing; Kramer, Hannah; Granderson, Jessica Building and Environment, Vol. 168 https://doi.org/10.1016/j.buildenv.2019.106505	journal	January 2020
A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults Wang, Shengwei; Zhou, Qiang; Xiao, Fu Energy and Buildings, Vol. 42, Issue 4 https://doi.org/10.1016/j.enbuild.2009.10.017	journal	April 2010
Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems Yuwono, Mitchell; Guo, Ying; Wall, Josh Applied Soft Computing, Vol. 34 https://doi.org/10.1016/j.asoc.2015.05.030	journal	September 2015
A systematic feature selection procedure for short-term data-driven building energy forecasting model development Zhang, Liang; Wen, Jin Energy and Buildings, Vol. 183 https://doi.org/10.1016/j.enbuild.2018.11.010	journal	January 2019
A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings Zhang, Liang; Frank, Stephen; Kim, Janghyun Building and Environment, Vol. 186 https://doi.org/10.1016/j.buildenv.2020.107338	journal	December 2020
A review of machine learning in building load prediction Zhang, Liang; Wen, Jin; Li, Yanfei Applied Energy, Vol. 285 https://doi.org/10.1016/j.apenergy.2021.116452	journal	March 2021
Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD) Zhao, Yang; Wang, Shengwei; Xiao, Fu Applied Energy, Vol. 112 https://doi.org/10.1016/j.apenergy.2012.12.043	journal	December 2013

Similar Records

Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Journal Article · Sun Aug 01 00:00:00 EDT 2021 · Advances in Applied Energy · OSTI ID:1820573

Zhang, Liang; Leach, Matt; Bae, Yeonjin; +7 more

Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings

Journal Article · Sat Oct 15 00:00:00 EDT 2022 · Energy · OSTI ID:1820573

Zhang, Liang; Leach, Matt; Chen, Jianli; +1 more

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

Technical Report · Wed Apr 01 00:00:00 EDT 2020 · OSTI ID:1820573

Im, Piljae; Bae, Yeonjin; Cui, Borui; +6 more

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
data-driven modeling
fault detection and diagnostics
Monte Carlo simulation
sensor accuracy
sensor fault
sensor selection

Title: Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation

Citation Formats

References (17)

Similar Records

Related Subjects