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Buildings directly and indirectly emit a large share of current CO2emissions. There is a high potential for CO2 savings through modern controlmethods in building automation systems (BAS) like model predictive control(MPC). For a proper control, MPC needs mathematical models to predict thefuture behavior of the controlled system. For this purpose, digital twins ofthe building can be used. However, with current methods in existing buildings,a digital twin set up is usually labor-intensive. Especially connecting thedifferent components of the technical system to an overall digital twin of thebuilding is time-consuming. Piping and instrument diagrams (P&ID) can providethe needed information, but it is necessary to extract the information andprovide it in a standardized format to process it further.  In this work, we present an approach to recognize symbols and connections ofP&ID from buildings in a completely automated way. There are various standardsfor graphical representation of symbols in P&ID of building energy systems.Therefore, we use different data sources and standards to generate a holistictraining data set. We apply algorithms for symbol recognition, line recognitionand derivation of connections to the data sets. Furthermore, the result isexported to a format that provides semantics of building energy systems.  The symbol recognition, line recognition and connection recognition show goodresults with an average precision of 93.7%, which can be used in furtherprocesses like control generation, (distributed) model predictive control orfault detection. Nevertheless, the approach needs further research.

Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams