Transfer Learning for HVAC System Fault Detection

Conference · Mon Jul 27 04:00:00 EDT 2020

DOI:https://doi.org/10.23919/ACC45564.2020.9147772· OSTI ID:1765329

Dowling, Chase P. ^[1]; Zhang, Baosen ^[2]

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1. BATTELLE (PACIFIC NW LAB)
2. University of Washington

Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity. Yet the lack of labeled data, such as normal versus faulty operational status, has slowed the application of machine learning to HVAC systems. In addition, for any particular building, there may be an insufficient number of observed faults over a reasonable amount of time for training. To overcome these challenges, we present a transfer methodology for a novel Bayesian classifier designed to distinguish between normal operations and faulty operations. The key is to train this classifier on a building with a large amount of sensor and fault data (for example, via simulation or standard test data) then transfer the classifier to a new building using a small amount of normal operations data from the new building. We demonstrate a proof-of-concept for transferring a classifier between architecturally similar buildings in different climates and show few samples are required to maintain classification precision and recall.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1765329
• Report Number(s): PNNL-SA-150637
• Country of Publication: United States
• Language: English

Related Subjects

transfer learning
Building HVAC
Bayesian framework