Fault detection and diagnostics (FDD) analytical tools for heating, ventilation and air
conditioning (HVAC) systems represent one of the most active areas of smart building
technology development. A diversity of techniques is used for FDD analytics, spanning physical
models, black box, and rule-based approaches, and researchers continuously strive to develop
improved algorithms. With FDD algorithm numbers now in the hundreds, there is a need for
performance evaluation of these algorithms in order to assess improvements, improve costeffectiveness,
and to prioritize investment in the further development of these technologies. A
persistent challenge of FDD advance has been the lack of common datasets to benchmark the
performance accuracy of FDD algorithms.
This paper summarizes the successful curation of HVAC operational data, paired with
validated ground-truth information regarding the presence and absence of faults. The current
dataset, consisting of both simulation and experimental data, will evolve to include a larger set of
HVAC systems with the objective of creating the largest publicly available dataset to be used by
FDD developers, users, and researchers to compare and contrast performance accuracy across
FDD algorithms, helping to drive improvements that will spur greater market adoption of FDD
tools. Furthermore, in order to avoid previously observed issues with contributed datasets and
ensure high quality and consistency of future submissions, the development of data validation
and ground-truth assessment protocol is detailed in this study.