Publications
2022
Huang, Jiajing; Wen, Jin; Yoon, Hyunsoo; Pradhan, Ojas; Wu, Teresa; O’Neill, Zheng; Candan, Kasim Selcuk
In: Energy and Buildings, vol. 259, pp. 111872, 2022, ISSN: 0378-7788.
Abstract | Links | BibTeX | Tags: Building AFDD, Machine learning, Real, Similarity, Simulated
@article{HUANG2022111872,
title = {Real vs. simulated: Questions on the capability of simulated datasets on building fault detection for energy efficiency from a data-driven perspective},
author = {Jiajing Huang and Jin Wen and Hyunsoo Yoon and Ojas Pradhan and Teresa Wu and Zheng O'Neill and Kasim Selcuk Candan},
url = {https://www.sciencedirect.com/science/article/pii/S0378778822000433},
doi = {https://doi.org/10.1016/j.enbuild.2022.111872},
issn = {0378-7788},
year = {2022},
date = {2022-01-01},
journal = {Energy and Buildings},
volume = {259},
pages = {111872},
abstract = {Literature on building Automatic Fault Detection and Diagnosis (AFDD) mainly focuses on simulated system data due to high expenses and difficulties of obtaining and analyzing real building data. There is a lack of validation on performances and scalabilities of data-driven AFDD approaches using simulated data and how it compares to that from real building data. In this study, we conduct two sets of experiments to seek answers to this question. We first evaluate data-driven fault detection strategies on real and simulated building data separately. We observe that the fault detection performances are not affected by fault detection strategies, sizes of training data, and the number of cross-validation folds when training and blind test data come from the same data source, namely, simulated or real building data. Next, we conduct a cross-dataset study, that is, develop the model using simulated data and tested on real building data. The results indicate the model trained on simulated data is not generalized to be applied for real building data for fault detection. Kolmogorov-Smirnov Test is conducted to confirm that there exist statistical differences between the simulated and real building data and identify a subset of features with similarities between the two datasets. Using the subset of the feature, cross-dataset experiments show fault detection improvements on most fault cases. We conclude that even if the system produces simulated data with the same fault symptoms from physical analysis perspectives, not all features from simulated datasets may not be beneficial for AFDD but only a subset of features contains valuable information from a machine learning perspective.},
keywords = {Building AFDD, Machine learning, Real, Similarity, Simulated},
pubstate = {published},
tppubtype = {article}
}
Literature on building Automatic Fault Detection and Diagnosis (AFDD) mainly focuses on simulated system data due to high expenses and difficulties of obtaining and analyzing real building data. There is a lack of validation on performances and scalabilities of data-driven AFDD approaches using simulated data and how it compares to that from real building data. In this study, we conduct two sets of experiments to seek answers to this question. We first evaluate data-driven fault detection strategies on real and simulated building data separately. We observe that the fault detection performances are not affected by fault detection strategies, sizes of training data, and the number of cross-validation folds when training and blind test data come from the same data source, namely, simulated or real building data. Next, we conduct a cross-dataset study, that is, develop the model using simulated data and tested on real building data. The results indicate the model trained on simulated data is not generalized to be applied for real building data for fault detection. Kolmogorov-Smirnov Test is conducted to confirm that there exist statistical differences between the simulated and real building data and identify a subset of features with similarities between the two datasets. Using the subset of the feature, cross-dataset experiments show fault detection improvements on most fault cases. We conclude that even if the system produces simulated data with the same fault symptoms from physical analysis perspectives, not all features from simulated datasets may not be beneficial for AFDD but only a subset of features contains valuable information from a machine learning perspective.