Abstract
Automated fault detection and diagnosis (AFDD) tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, access to high-quality training data for such algorithms is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus® and OpenStudio® to generate a cost-effective training data set for developing AFDD algorithms. Part II (this paper) first presents a methodology of validating fault models with OpenStudio and then presents validation results, which are compared against measurements from a reference building. We discuss the results of our experiments with eight different faults in the reference building (a total of 39 different baseline and faulted scenarios), including our methodology for using fault models along with the reference building model to simulate the same faulted scenarios. Then, we present validation of the fault models by comparing results of simulations and experiments either quantitatively or qualitatively.
| Original language | English |
|---|---|
| Article number | 239 |
| Journal | Buildings |
| Volume | 9 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 1 2019 |
Funding
Funding: This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding was provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Building Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allows others to do so, for U.S. Government purposes. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding was provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Building Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allows others to do so, for U.S. Government purposes. The authors greatly appreciate the experimental support of Jaewan Joe of Oak Ridge National Laboratory during the last phase of this project.
| Funders | Funder number |
|---|---|
| U.S. Government | |
| U.S. Department of Energy | DE-AC36-08GO28308 |
| Office of Energy Efficiency and Renewable Energy | |
| Oak Ridge National Laboratory | |
| National Renewable Energy Laboratory |
Keywords
- Automated fault detection and diagnosis
- Building energy modeling
- EnergyPlus
- Fault experiment
- Fault model
- OpenStudio
- Validation
