mfuchs/uesgraphs

!E.ON EBC RWTH Aachen University

uesgraphs

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uesgraphs is a Python package to describe Urban Energy Systems and manage their data in a Python graph structure. We extend the https://networkx.github.io/ Graph class and add basic methods to represent buildings and energy networks in the graph. uesgraphs can be used as a foundation to analyze energy network structures, evaluate district energy systems or generate simulation models.

uesgraphs is being developed at RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate.

Getting started

Install uesgraphs

uesgraphs relies on other packages to function correctly. On Windows, it may be necessary to install shapely and pyproj before uesgraphs. We recommend to download the .whl files for installing shapely and pyproj from the Unofficial Windows Binaries for Python Extension Packages for your system and python versions. Install both .whl files with pip install <path/to/file.whl>.

One way to get uesgraphs set up is to use a fresh Conda environment by following these steps:

• Install Miniconda or update your conda installation with conda update conda
• Create a new environment with conda create -n <nameOfEnvironment> python=3.6
• Activate the environment with source activate <nameOfEnvironment> on Linux or activate <nameOfEnvironment> on Windows
• Clone uesgraphs with git clone https://github.com/RWTH-EBC/uesgraphs.git
• Install uesgraphs with pip install -e <path/to/your/uesgraphs>
• Verify your uesgraphs installation by running the automated tests. Go to your uesgraphs top-level folder and type py.test --mpl

Usage

You can assemble a graph of an urban energy system by adding buildings, network nodes and edges to an UESGraph object. The following code builds a heating network from one building to another, connected via one network node:

Python
import uesgraphs as ug
from shapely.geometry import Point

graph = ug.UESGraph()

supply = graph.add_building(
    name='Supply',
    position=Point(0, 10),
    is_supply_heating=True,
)
demand = graph.add_building(
    name='Building 1',
    position=Point(50, 15),
)
heating_node = graph.add_network_node(
    network_type='heating',
    position=Point(30, 5),
)

graph.add_edge(supply, heating_node)
graph.add_edge(heating_node, demand)

You can go on to plot this energy system with

Python
vis = ug.Visuals(graph)
vis.show_network(
    show_plot=True,
    scaling_factor=30,
    )

!Example graph

Instead of building a graph from scratch, uesgraphs comes with an example containing all supported energy network types. You can create this example graph with

Python
import uesgraphs as ug
from shapely.geometry import Point

graph = ug.simple_dhc_model()
graph = ug.add_more_networks(graph)

vis = ug.Visuals(example_district)
fig = vis.show_network(
    show_plot=True,
    scaling_factor=10,
)

This leads to the following plot:

!Example graph

You can extract single networks into their own subgraph with

Python
heating_network_1 = graph.create_subgraphs('heating')['default']

In the example above, this extracts the first of the two heating networks shown in red:

!Example graph

You can use this graph framework to add data to the nodes and edges, e.g.

Python
import uesgraphs as ug
from shapely.geometry import Point

graph = ug.UESGraph()

demand = graph.add_building(
    name='Building 1',
    position=Point(50, 15),
)

graph.nodes[demand]['heat_load_kW'] = 200

This can be used as a foundation to analyze networks or to generate models.

License

uesgraphs is released by RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, under the https://github.com/RWTH-EBC/uesgraphs/blob/master/LICENSE.md.

Reference

To reference uesgraphs, please cite the following paper (doi https://doi.org/10.1016/j.energy.2016.04.023):

Marcus Fuchs, Jens Teichmann, Moritz Lauster, Peter Remmen, Rita Streblow, Dirk Müller: Workflow automation for combined modeling of buildings and district energy systems, Energy, Volume 117, Part 2, 2016, Pages 478-484.

The BibTex for this paper is:

BibTex
@article{Fuchs2016,
  doi = {10.1016/j.energy.2016.04.023},
  url = {https://doi.org/10.1016/j.energy.2016.04.023},
  year  = {2016},
  month = {dec},
  publisher = {Elsevier {BV}},
  volume = {117},
  pages = {478--484},
  author = {Marcus Fuchs and Jens Teichmann and Moritz Lauster and Peter Remmen and Rita Streblow and Dirk M\"{u}ller},
  title = {Workflow automation for combined modeling of buildings and district energy systems},
  journal = {Energy}
}

Acknowledgements

This work was supported by the Helmholtz Association under the Joint Initiative “Energy System 2050 – A Contribution of the Research Field Energy”.

Parts of uesgraphs have been developed within public funded projects and with financial support by BMWi (German Federal Ministry for Economic Affairs and Energy).