  Buildings directly and indirectly emit a large share of current CO2
emissions. There is a high potential for CO2 savings through modern control
methods in building automation systems (BAS) like model predictive control
(MPC). For a proper control, MPC needs mathematical models to predict the
future behavior of the controlled system. For this purpose, digital twins of
the building can be used. However, with current methods in existing buildings,
a digital twin set up is usually labor-intensive. Especially connecting the
different components of the technical system to an overall digital twin of the
building is time-consuming. Piping and instrument diagrams (P&ID) can provide
the needed information, but it is necessary to extract the information and
provide it in a standardized format to process it further.
  In this work, we present an approach to recognize symbols and connections of
P&ID from buildings in a completely automated way. There are various standards
for graphical representation of symbols in P&ID of building energy systems.
Therefore, we use different data sources and standards to generate a holistic
training data set. We apply algorithms for symbol recognition, line recognition
and derivation of connections to the data sets. Furthermore, the result is
exported to a format that provides semantics of building energy systems.
  The symbol recognition, line recognition and connection recognition show good
results with an average precision of 93.7%, which can be used in further
processes like control generation, (distributed) model predictive control or
fault detection. Nevertheless, the approach needs further research.
