@article{32373,
  keywords = {Fault detection and diagnostics (FDD), Algorithm testing, Test procedure, Open dataset},
  author = {Jessica Granderson and Guanjing Lin and Ari Harding and Piljae Im and Yan Chen},
  title = {Building fault detection data to aid diagnostic algorithm creation and performance testing},
  abstract = {<p>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. &nbsp;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. &nbsp;</p><p class="Normal1">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.</p>},
  year = {2020},
  journal = {Nature: Scientific Data},
  volume = {Vol.7 No.65},
  month = {03/2020},
  language = {eng},
}