A Dymola-Python framework for data-driven model creation and co-simulation


  • Sandra Wilfling
  • Basak Falay
  • Qamar Alfalouji
  • Gerald Schweiger




Energy Systems, Modeling and Simulation, Data-driven Modeling, Co-Simulation


The introduction of cyber-physical systems has been a recent development in energy systems. Cyber-physical systems contain digital components for applications such as monitoring or control. In many cases, modeling multiple aspects of such cyber-physical systems poses a challenge to conventional simulation tools. In addition, recent modeling approaches, such as data-driven modeling, are being applied. The combination of such data-driven models, which may consist of a different architecture than traditional models, with traditional models can be implemented through co-simulation methods. In co-simulation, components created from different simulation tools can be combined and coupled through standardized interfaces. This work presents a framework for data-driven model generation and co-simulation. The framework is implemented in Python and Dymola and is based on the Functional Mock-up Interface (FMI) standard. The framework implements the creation of data-driven models in Python, the generation of Functional Mock-up Units (FMUs) through the frameworks uniFMU and pythonFMU, as well the creation of a testbench model in Dymola and the co-simulation of this model. The framework is demonstrated on the application of a solar collector from a single family house heating system.