  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.
