Examples
Python Script Examples
Creating and Visualizing a Simple Heating Network from the readme.md with uesgraphs |
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Creating and Visualizing a more sophisticated Network with uesgraphs including heating and cooling |
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Extend the e2_simple_dhc model for an electric grid and visualize |
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""Write example district heating cooling network to JSON file. |
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Usage of street nodes |
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Demonstration for adding additional attributes to building nodes (only terminal output) |
Creating advanced plots of uesgraphs. |
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How to load UESGraphs from JSON and OSM files. |
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Generate complex Urban Energy System (UES) Graph from OpenStreetMap (OSM) data. |
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Example Network Generator for UESGraphs :2: (WARNING/2) Title underline too short. Example Network Generator for UESGraphs ===================================== |
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Modelica Model Generation using UESGraphs :2: (WARNING/2) Title underline too short. Modelica Model Generation using UESGraphs ======================================= |
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Comprehensive Mako Template Generation for UESGraphs Modelica Components :2: (WARNING/2) Title underline too short. Comprehensive Mako Template Generation for UESGraphs Modelica Components ===================================================================== |
Example 13: Analysis and Visualization of Simulation Results
This example processes and visualizes simulation results from Dymola for UESGraphs-generated district heating network models. It demonstrates the analysis workflow from loading simulation data to creating network visualizations with key performance indicators.
Prerequisites:
Simulation period can be specified (default: one week)
Visualization uses time-averaged values for certain properties
AixLib version must be specified for correct data mapping
All paths are relative to the workspace directory
Location: uesgraphs/examples/e13_analyze_uesgraphs_example.py
GitHub: View on GitHub
Jupyter Notebook Examples
Example 14: Hydronic Sizing Example
This interactive notebook demonstrates demand-based mass flow estimation and hydronic sizing capabilities.
Topics covered:
Physically accurate mass flow calculations based on individual demand nodes
Automated pipe sizing using manufacturer catalogs
Robust network design using maximum flow principles
Flexible scenario analysis (peak vs average loads)
Location: uesgraphs/examples/e14_hydronic_sizing_example.ipynb
GitHub: View on GitHub
Example 15: Import from GeoJSON
This interactive notebook demonstrates how to import district heating networks from GeoJSON files.
Topics covered:
Importing network topology from GeoJSON files
Loading building data and supply points from separate GeoJSON files
Automatic network generation and validation
Generating visualizations during the import process
Location: uesgraphs/examples/e15_from_geojson_example.ipynb
GitHub: View on GitHub
Interactive Plotting Example
Additional interactive plotting demonstrations using Jupyter notebooks.
Location: uesgraphs/examples/interactive_plotting_example.ipynb
GitHub: View on GitHub