uesgraphs: Automated graph-based simulation model generation and analysis tool
uesgraphs is a Python package developed at RWTH-EBC. It utilizes a graph structure to manage data for buildings, energy networks, and infrastructure, enabling the automated generation of dynamic district simulation models.
### General Motivation
The decarbonization of energy supply demands integral approaches and automation to manage the increasing complexity of urban energy systems. uesgraphs addresses this by providing a flexible, model-neutral description of complex energy infrastructure that is ready for simulation workflows.
### Getting Started
The best way to start is by installing the package and checking out the examples.
# 1. Create and activate a new virtual environment
conda create -n uesgraphs-env python=3.13
conda activate uesgraphs-env
# 2. Install uesgraphs
pip install uesgraphs
### Structure and Workflow
uesgraphs is built with networkx as its core library. The typical workflow for the tool involves:
The comprehensive workflow is broken down into these five stages:
Input: Data ingestion from various sources, including Open Street Map (OSM) based data, manual imports, and JSON imports. This forms the foundation of the system model.
Graph preparation: This stage involves crucial preprocessing steps like hydronic sizing, topology clean-up, and network simplification to ensure a robust and accurate model structure within the
uesgraphs.UESGraph.Simulation: The prepared graph is used to create dynamic simulation model for the district based on the graph generated which can be then simulated in using tools like Dymola. This process can also be automated.
Analysis: Post-simulation data (such as result .mat files) is handled for tasks including constraint analysis and Key Performance Indicator (KPI) evaluation.
Visualisation: The final stage focuses on presenting results effectively, offering features like color-coded plots, 3-D plots, and exploded views.
API Documentation
Contents: