LBNL Fault Detection and Diagnostics Datasets
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.
Citation Formats
Lawrence Berkeley National Laboratory. (2022). LBNL Fault Detection and Diagnostics Datasets [data set]. Retrieved from https://dx.doi.org/10.25984/1881324.
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., 01 Aug, 2022. Web. doi: 10.25984/1881324.
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. https://dx.doi.org/10.25984/1881324
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. https://dx.doi.org/10.25984/1881324. https://data.openei.org/submissions/5763.
@div{oedi_5763, title = {LBNL Fault Detection and Diagnostics Datasets}, author = {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.}, 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}, url = {https://data.openei.org/submissions/5763}, journal = {}, number = , volume = , place = {United States}, year = {2022}, month = {08}}
https://dx.doi.org/10.25984/1881324
Details
Data from Aug 1, 2022
Last updated Feb 12, 2024
Submitted Aug 8, 2022
Organization
Lawrence Berkeley National Laboratory
Contact
Jessica Granderson
Authors
Research Areas
Keywords
Commercial Buildings, Fault Detection and Diagnostics, HVAC, Brick Schema, Algorithm testing, Performance evaluation, AHU, RTU, Fan coil, VAV box, Chiller plant, Boiler plant, AC, fault detection, diagnostics, detection, benchmark, building, building energy, building energy efficiency, energy efficiency, building efficiency, air handler unit, heating, cooling, heating and coolingDOE Project Details
Project Name Fault Detection and Diagnostics: Test Datasets and Prioritization Methods
Project Number FY22 AOP 3.2.6.1.
