  Many sensors/meters are deployed in commercial buildings to monitor and
optimize their performance. However, because sensor metadata is inconsistent
across buildings, software-based solutions are tightly coupled to the sensor
metadata conventions (i.e. schemas and naming) for each building. Running the
same software across buildings requires significant integration effort.
  Metadata normalization is critical for scaling the deployment process and
allows us to decouple building-specific conventions from the code written for
building applications. It also allows us to deal with missing metadata. One
important aspect of normalization is to differentiate sensors by the typeof
phenomena being observed. In this paper, we propose a general, simple, yet
effective classification scheme to differentiate sensors in buildings by type.
We perform ensemble learning on data collected from over 2000 sensor streams in
two buildings. Our approach is able to achieve more than 92% accuracy for
classification within buildings and more than 82% accuracy for across
buildings. We also introduce a method for identifying potential misclassified
streams. This is important because it allows us to identify opportunities to
attain more input from experts -- input that could help improve classification
accuracy when ground truth is unavailable. We show that by adjusting a
threshold value we are able to identify at least 30% of the misclassified
instances.
