Welcome

Welcome#

Welcome to the website accompanying the course Data Science for Energy System Modelling. This course is being developed by Dr. Fabian Neumann and offered as part of the curriculum of the Department of Digital Transformation of Energy Systems at TU Berlin.

On this website you will find practical introductions to many Python packages that are useful for dealing with energy data and building energy system models. Course materials other than practical introductions to Python packages for students at TU Berlin are provided on ISIS (winter semeter 2025/2026).

The course covers tutorials and examples for getting started with Python, numpy, matplotlib, pandas, geopandas, cartopy, rasterio, pysheds, atlite, networkx, linopy, pypsa, plotly, hvplot, and streamlit. Topics covered include:

  • time series analysis (e.g., demand, wind and solar production)

  • tabular data (e.g., power plants, industrial sites, LNG terminals)

  • geographical data (e.g., power plants, industrial sites, LNG terminals)

  • data visualisation (e.g., static and interactive plots, maps, dashboards)

  • converting weather data to renewable generation availability

  • land eligibility analysis (e.g., where can we build wind turbines or solar parks)

  • optimisation (e.g. linear programming and using solvers)

  • electricity market modelling (e.g., how dispatch is determined and prices form)

  • power flow modelling (e.g., how electricity can cause grid congestion)

  • capacity expansion planning (e.g., where to build new generation, storage, and transmission)

  • sector-coupling (e.g., hydrogen, heat pumps, electric vehicles)

Quickstart with Google Colab#

You can begin with the course without a local Python installation using online services like Google Colab (colab.google) which provide an online Python version in a Jupyter Notebook environment. This requires a Google account.

Navigate to the other pages. You will find a rocket-shaped button at the top of each page that says “Open in Colab”. Click this button to open the page in Google Colab.

Getting the Code#

To get the code and material for this course, you can download the full GitHub repository or parts of it. There are several ways to do this:

Individual File Download (beginner friendly):#

You can download individual files directly from website by clicking on the download button at the top-right corner of each page.

Note

This method is suitable if you want to start step by step and want to update them selectively once in a while.

ZIP Option (easy, but static):#

Download the ZIP file from GitHub and extract it to your computer in a directory of your choice.

Warning

This method does not allow you to easily update the course materials later on. You will need to re-download the ZIP file and re-extract it to get the latest updates.

Git Option (more advanced):#

Git is a version control system that allows you to manage and track changes to your code. If you don’t have Git installed, you can install it from git-scm.com. Once installed, you can navigate to a directory where you want to store the course materials and clone the repository using Git. Cloning means you create a local copy of the repository on your computer. It can be done by running the following commands:

# navigate to your desired directory
cd path/to/your/directory

# clone the repository
git clone https://github.com/fneum/data-science-for-esm.git

# enter the cloned repository
cd data-science-for-esm

# update the repository to get the latest changes
git pull

The # symbol indicates comments and should not be typed.

Note

What is navigation in the command line and how to do it?

The command line (also known as terminal, shell, or console) is a text-based interface that allows you to interact with your computer’s operating system by typing commands. To navigate the file system using the command line, you use commands to change directories and list files. The following are some basic commands:

  • cd my_directory: Change directory to my_directory

  • cd ..: Move up one directory

  • ls (Linux/Mac) or dir (Windows): List files and directories in the current directory

  • mkdir my_new_directory: Create a new directory named my_new_directory

Paths (i.e., the directions to where files and directories are stored) can be absolute (e.g., C:\Users\YourName\Documents on Windows or /home/yourname/documents on Linux/Mac) or relative to the current directory.

On Windows, you use backslashes \ to separate directories in paths, while on Linux and MacOS, you use forward slashes /.

For a more detailed introduction to using the command line, see this tutorial.

Alternative Setup Options#

Using pip#

Warning

Some geographical packages used in this course (e.g., rasterio, geopandas, cartopy) may require additional system dependencies that are not handled by pip. Unlike with conda, you may need to install these dependencies manually, e.g. “GDAL”. Only use this method if you are experienced with package management!

For using pip as package manager, first ensure you have a working Python installation (version 3.13 is recommended). You can download Python from python.org for your operating system.

Once Python is installed, you can open the command prompt / terminal and verify the installation by typing:

python --version
python -m pip --version
# or
pip --version

Now, you can upgrade pip to the latest version by running:

pip install --upgrade pip

Similar to the conda environment setup, you can install all required packages using a requirements.txt file. This is the same for all operating systems.

For our course, you can find it here. There is a download button at the top-right corner.

After navigating to the folder where the requirements.txt file is stored (i.e. using cd path/to/your/directory), you can install the required packages with

pip install -r requirements.txt

You can add more packages later using pip install package_name, e.g.

pip install pandas

Once this is done, you can start a new Jupyter Lab session in your browser:

jupyter lab

Unlike with conda, pip does not provide isolated environments by default.

Using uv#

Warning

Some geographical packages used in this course (e.g., rasterio, geopandas, cartopy) may require additional system dependencies that are not handled by pip. Unlike with conda, you may need to install these dependencies manually, e.g. “GDAL”. Only use this method if you are experienced with package management!

For using uv as package manager, first follow the installation instructions. These differ depending on your operating system.

Once uv is installed, navigate to your local copy of the course repository and verify that it’s working:

uv --version

Now, install all required packages and set up the environment by running:

uv sync

It is important that you run this command in the root folder of the course repository, where the pyproject.toml and uv.lock files are located.

You can also add more packages later using uv pip install package_name, e.g.

uv pip install pandas

Once this is done, you can start a new Jupyter Lab session in your browser with this environment by running:

uv run jupyter lab

VS Code#

An alternative to using Jupyter Lab is to use Visual Studio Code (VS Code) as your development environment. You still need to manage your Python installation and packages using either conda, pip, or uv as described above.

To work with Jupyter Notebooks in VS Code, you need to install the Jupyter extension. You can install it directly from the Extensions view in VS Code by searching for “Jupyter” and clicking “Install”. You also need to install the Python extension.

Now, whenever you open a Jupyter Notebook file (.ipynb), VS Code will automatically provide you with an interactive interface to run and edit your notebooks. To link the notebook to the correct Python environment, click on the kernel name in the top-right corner of the notebook interface and select the appropriate environment (i.e., esm-ws-25-26).

This setup is a lot more powerful than Jupyter Lab alone, as it provides many additional features such as code completion and AI-assisted coding.

Github Codespaces#

Github Codespaces provides a cloud-based development environment that can be configured to run all code examples for this course. It is an alternative to Google Colab (described above) or local development on your computer. A Github account is required. To use it, simply click the button below to create your own copy of the course repository on Github.

Open in GitHub Codespaces

This will create a fork of the repository in your own Github account; the link will look like this: https://github.com/YOUR_USERNAME/data-science-for-esm.

Then, click on the green “Code” button and select “Open with Codespaces” and “New codespace”. This will create a new Codespace for you. It may take a few minutes to set up the environment for the first time. You get an online VS Code environment with all required packages installed.

If you want to sync your forked repository with the original repository without using the command line, you can use the “Sync fork” button in your forked repository on Github.

Note

The free runtime for Github Codespaces is limited to 120 hours per month per user. There is a Github Software Student Pack that provides 60 additional free hours for students. See https://education.github.com/pack for more information.

Warning

Github Codespaces sessions will time out after a period of inactivity. Unsaved work may be lost. Make sure to frequently save your work.

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