ModelSIM – Development of a bi-directional interface for the model-based coupling of municipal planning, simulation and analysis processes - Subproject Interfaces for model generation and dynamic simulation simulation
2017 till 2019
BMWi, Förderkennzeichen 03ET1410B
- Karlsruhe Institut für Technologie (KIT) - Fachgebiet Building Lifecycle Management (BLM)
- RWTH Aachen University - Lehrstuhl für Energieeffizientes Bauen E3D
- RWTH Aachen University - E.ON ERC, Institute for Energy Efficient Buildings and Indoor Climate EBC
- GEF Ingenieur AG
Efficient computations and flexible simulation platforms for urban scale energy performance simulations are highly important for urban planning and energy analysis. Currently, research groups and simulation experts face the deficient interoperability between different use cases and energy demand calculations on a city scale. To address this problem, this project focusses on the development of a bidirectional interface for the model based coupling of municipal planning, simulation and analysis. Using open source data models of the CityGML standard and the simulation environment language Modelica, the tool chain allows users to compute dynamic energy demands for different test cases.
Based on the CityGML standard, the bidirectional interface uses specific building information, statistical building typology data and Application Domain Extensions which are developed in parallel (such as Energy ADE where the current development is hosted by E3D-RWTH GitLab Server). As a starting point, the CityGML importer extracts building data and energy related information which is further transferred to TEASER/TEASER+. This information in different granularities and levels of detail (LoD) are used to develop Modelica models using standard libraries such as AixLiB, BuildingPy, etc. A statistical enrichment using predefined libraries is carried out for building the simulation models. Furthermore, a closed loop is created by exporting the energy demand computations back to the input CityGML file. As a result, efficient analysis using visualization tools based on energy demands can be made to optimize district heating networks, district renewable potential and urban planning.