Improving the Prediction Accuracy of Data-Driven Fault Diagnosis for HVAC Systems by Applying the Synthetic Minority Oversampling Technique

Shahrad Shakerian, Houtan Jebelli, William E. Sitzabee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Faulty operation of heating, ventilation, and air conditioning (HVAC) systems in a building can lead to thermal discomfort, wasted energy, and shorter equipment life. Early fault diagnosis in HVAC systems is critical to maintaining indoor environmental comfort, saving energy, and preventing further deterioration of the system. Data-driven predictive models have emerged as a popular approach to fault diagnosis of HVAC systems. For an HVAC system, failures are a small probability event. Therefore, the prediction accuracy of these data-driven approaches is affected by imbalanced data sets. The normal data far outweigh the fault data. Predictive models trained with these imbalanced data sets will perform poorly in fault diagnosis of an HVAC system. To address this limitation, this study aims to examine the potential of the synthetic minority oversampling technique (SMOTE) for sampling fault data points to improve the accuracy of the fault predictive model. To that end, six months of operational data (for example, humidity and temperature) were collected from embedded sensors in the HVAC system. A SMOTE algorithm was applied to increase the ratio of failure to normal data from 0.02 to 0.3. Both the original and improved data sets were used to train the fault predictive model based on different supervised learning algorithms. Results indicated that data sets improved through the SMOTE algorithm increased the accuracy of the predictive model by about 20%. This improvement can lay the groundwork for increasingly proactive facility maintenance.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021
EditorsR. Raymond A. Issa
PublisherAmerican Society of Civil Engineers (ASCE)
Pages90-97
Number of pages8
ISBN (Electronic)9780784483893
DOIs
StatePublished - 2021
Event2021 International Conference on Computing in Civil Engineering, I3CE 2021 - Orlando, United States
Duration: Sep 12 2021Sep 14 2021

Publication series

NameComputing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021

Conference

Conference2021 International Conference on Computing in Civil Engineering, I3CE 2021
Country/TerritoryUnited States
CityOrlando
Period9/12/219/14/21

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications

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