Transfer Learning for HVAC System Fault Detection
This addresses the challenge of limited fault data for building energy efficiency, though it is incremental as it builds on existing transfer learning methods.
The paper tackles the problem of insufficient labeled data for HVAC fault detection by proposing a transfer learning approach using a Bayesian classifier, demonstrating that few samples are needed to maintain precision and recall when transferring between similar buildings in different climates.
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.