High-Fidelity Building Emulator
This dataset provides high-fidelity time series data for an emulated commercial office building sited in the Chicago, IL area during a Typical Meteorological Year (TMY). This dataset consists of air-side HVAC measurements and control inputs, and it includes normal operations as well as various implemented faults (with associated ground truth measurements) implemented on selected days. This data could be used to quantify and compare the impacts of different faults, and it could also be used as training or validation data for machine learning algorithms (e.g., reduced-order modelling, fault detection and diagnosis).
Citation Formats
Building Technologies Office (BTO). (2022). High-Fidelity Building Emulator [data set]. Retrieved from https://dx.doi.org/10.17041/1873744.
Bakker, Craig, Huang, Sen, and Vasisht, Soumya. High-Fidelity Building Emulator. United States: N.p., 16 Jun, 2022. Web. doi: 10.17041/1873744.
Bakker, Craig, Huang, Sen, & Vasisht, Soumya. High-Fidelity Building Emulator. United States. https://dx.doi.org/10.17041/1873744
Bakker, Craig, Huang, Sen, and Vasisht, Soumya. 2022. "High-Fidelity Building Emulator". United States. https://dx.doi.org/10.17041/1873744. https://bbd.labworks.org/ds/hfbe.
@div{oedi_5722, title = {High-Fidelity Building Emulator}, author = {Bakker, Craig, Huang, Sen, and Vasisht, Soumya.}, abstractNote = {This dataset provides high-fidelity time series data for an emulated commercial office building sited in the Chicago, IL area during a Typical Meteorological Year (TMY). This dataset consists of air-side HVAC measurements and control inputs, and it includes normal operations as well as various implemented faults (with associated ground truth measurements) implemented on selected days. This data could be used to quantify and compare the impacts of different faults, and it could also be used as training or validation data for machine learning algorithms (e.g., reduced-order modelling, fault detection and diagnosis).}, doi = {10.17041/1873744}, url = {https://bbd.labworks.org/ds/hfbe}, journal = {}, number = , volume = , place = {United States}, year = {2022}, month = {06}}
https://dx.doi.org/10.17041/1873744
Details
Data from Jun 16, 2022
Last updated Sep 15, 2022
Submitted Jun 16, 2022
Organization
Building Technologies Office (BTO)
Contact
Craig Bakker
Authors
Original Source
https://bbd.labworks.org/ds/hfbeKeywords
DOE Project Details
Project Name Building Data Platform
Project Number 69035