  This article presents a Unified Architecture for automated point tagging of
Building Automation System data, based on a combination of data-driven
approaches. Advanced energy analytics applications-including fault detection
and diagnostics and supervisory control-have emerged as a significant
opportunity for improving the performance of our built environment. Effective
application of these analytics depends on harnessing structured data from the
various building control and monitoring systems, but typical Building
Automation System implementations do not employ any standardized metadata
schema. While standards such as Project Haystack and Brick Schema have been
developed to address this issue, the process of structuring the data, i.e.,
tagging the points to apply a standard metadata schema, has, to date, been a
manual process. This process is typically costly, labor-intensive, and
error-prone. In this work we address this gap by proposing a UA that automates
the process of point tagging by leveraging the data accessible through
connection to the BAS, including time series data and the raw point names. The
UA intertwines supervised classification and unsupervised clustering techniques
from machine learning and leverages both their deterministic and probabilistic
outputs to inform the point tagging process. Furthermore, we extend the UA to
embed additional input and output data-processing modules that are designed to
address the challenges associated with the real-time deployment of this
automation solution. We test the UA on two datasets for real-life buildings: 1.
commercial retail buildings and 2. office buildings from the National Renewable
Energy Laboratory campus. The proposed methodology correctly applied 85-90
percent and 70-75 percent of the tags in each of these test scenarios,
respectively.
