Abstract
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. 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. 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.
| Original language | English |
|---|---|
| Article number | 65 |
| Journal | Scientific Data |
| Volume | 7 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 1 2020 |
Funding
This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Office, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The authors wish to acknowledge for their contributions to the dataset and its documentation: Professor Jin Wen of Drexel University; Draguna Vrabie, and Sen Huang of Pacific Northwest National Laboratory; Stephen Frank and Willy Bernal Heredia of National Renewable Energy Laboratory. Finally, we thank Marina Sofos, Erika Gupta, and Amy Jiron of the Building Technologies Office for their generous support.