LBNL Fault Detection and Diagnostics Datasets

Granderson, Jessica; Lin, Guanjing; Chen, Yimin; Casillas, Armando; Im, Piljae; Jung, Sungkyun; Benne, Kyle; Ling, Jiazhen; Gorthala, Ravi; Wen, Jin; Chen, Zhelun; Huang, Sen; Vrabie, Draguna

doi:10.25984/1881324

Title: LBNL Fault Detection and Diagnostics Datasets

Dataset
Other Related Research

Abstract

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.

Authors:

Granderson, Jessica;

; Chen, Yimin; Casillas, Armando; Im, Piljae; Jung, Sungkyun; Benne, Kyle; Ling, Jiazhen; Gorthala, Ravi; Wen, Jin; Chen, Zhelun; Huang, Sen; Vrabie, Draguna

Lawrence Berkeley National Laboratory

Publication Date:: Mon Aug 01 00:00:00 EDT 2022

Other Number(s):: 5763

Research Org.:: DOE Open Energy Data Initiative (OEDI); Lawrence Berkeley National Laboratory

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Multiple Programs (EE)

Collaborations:: Lawrence Berkeley National Laboratory

Subject:: AC; AHU; Algorithm testing; Array; Boiler plant; Brick Schema; Chiller plant; Commercial Buildings; Fan coil; Fault Detection and Diagnostics; HVAC; Performance evaluation; RTU; VAV box; air handler unit; benchmark; building; building efficiency; building energy; building energy efficiency; cooling; detection; diagnostics; energy efficiency; fault detection; heating; heating and cooling

OSTI Identifier:: 1881324

DOI:: https://doi.org/10.25984/1881324

Citation Formats


                    Granderson, Jessica, Lin, Guanjing, Chen, Yimin, Casillas, Armando, Im, Piljae, Jung, Sungkyun, Benne, Kyle, Ling, Jiazhen, Gorthala, Ravi, Wen, Jin, Chen, Zhelun, Huang, Sen, and Vrabie, Draguna. LBNL Fault Detection and Diagnostics Datasets.  United States: N. p., 2022. 
        Web.  doi:10.25984/1881324.

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                    Granderson, Jessica, Lin, Guanjing, Chen, Yimin, Casillas, Armando, Im, Piljae, Jung, Sungkyun, Benne, Kyle, Ling, Jiazhen, Gorthala, Ravi, Wen, Jin, Chen, Zhelun, Huang, Sen, & Vrabie, Draguna. LBNL Fault Detection and Diagnostics Datasets.  United States.  doi:https://doi.org/10.25984/1881324

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                    Granderson, Jessica, Lin, Guanjing, Chen, Yimin, Casillas, Armando, Im, Piljae, Jung, Sungkyun, Benne, Kyle, Ling, Jiazhen, Gorthala, Ravi, Wen, Jin, Chen, Zhelun, Huang, Sen, and Vrabie, Draguna. 2022.  
"LBNL Fault Detection and Diagnostics Datasets".  United States.  doi:https://doi.org/10.25984/1881324.  https://www.osti.gov/servlets/purl/1881324. Pub date:Mon Aug 01 00:00:00 EDT 2022

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@article{osti_1881324,

  title        = {LBNL Fault Detection and Diagnostics Datasets},

  author       = {Granderson, Jessica and Lin, Guanjing and Chen, Yimin and Casillas, Armando and Im, Piljae and Jung, Sungkyun and Benne, Kyle and Ling, Jiazhen and Gorthala, Ravi and Wen, Jin and Chen, Zhelun and Huang, Sen and Vrabie, Draguna},

  abstractNote = {These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.},

  doi          = {10.25984/1881324},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Aug 01 00:00:00 EDT 2022},

  month        = {Mon Aug 01 00:00:00 EDT 2022}

}

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DOI: https://doi.org/10.25984/1881324

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