Evaluation and simulation of building automation systems based on their AutomationML description

Fuentes, Damian Eduardo Diaz; Becker, Ulrik; Diekhake, Patrick; Gunther, Michelle; Scholz, André; Schmidt, Philipp Puntel; Fay, Alexander

doi:10.1109/etfa.2016.7733671

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…A digital twin can connect physical objects of an energy system with its virtual objects [11] so that there is a seamless transmission of data between them [12]. With help of a precise digital twin of building energy system, building automation can be optimized [13]. Machine learning applications can support to generate a digital twin.…”

Section: Introductionmentioning

confidence: 99%

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

Stinner,

Wiecek,

Baranski

et al. 2021

Preprint

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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 laborintensive. 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.

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Section: Introductionmentioning

confidence: 99%

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

Stinner,

Wiecek,

Baranski

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The approach addresses the reuse of the behavior models for the development of system modifications using control agents. A promising approach in the domain of building automation is [15], hence, a coupling between product and production system models is not considered. Whereas the aforementioned approaches mainly focused on automation systems based on the IEC 61131 standard, other works which address event-driven implementations conforming to the IEC 61499 standard [16,17] exist but have not been widely accepted in industry, yet.…”

Section: Model-based Development Of Flexible Automated Production Sysmentioning

confidence: 99%

Towards the co-evolution of industrial products and its production systems by combining models from development and hardware/software deployment in cyber-physical systems

Vogel‐Heuser

Wildermann

Teich

2017

Prod. Eng. Res. Devel.

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Automatic Generation of Pipelines Into a 3D Industrial Process Model

2017

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Evaluation and simulation of building automation systems based on their AutomationML description

Cited by 5 publications

References 8 publications

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

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

Towards the co-evolution of industrial products and its production systems by combining models from development and hardware/software deployment in cyber-physical systems

Automatic Generation of Pipelines Into a 3D Industrial Process Model

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