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.

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. wind and solar production)

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

  • geographical data (e.g. location of power plants)

  • data visualisation

  • converting weather data to renewable generation

  • land eligibility analysis (e.g. where can we build wind turbines)

  • optimisation

  • electricity market modelling

  • power flow modelling (linearised)

  • capacity expansion planning

  • sector-coupling

  • interactive visualisation and dashboarding

Installing the package manager conda#

Python and nearly all of the software packages in the scientific python ecosystem are open-source. Coordinating the compatibility between these different packages and their multiple versions used to be a nightmare! Fortunately, the problem is solved by using a Python distribution and/or package manager. You should use a package manager!


The easiest way to set up a full-stack scientific Python deployment is to use a Python distribution. This is an installation of Python with a set of curated packages which are guaranteed to work together.

For instance, you can install on your computer the popular Anaconda Python Distribution. Follow the link above to obtain a one-click installers for your operating system.

For Linux and MacOS users, you can access the command line by opening the terminal program.

For Windows users, you should first install Anaconda (described above) or miniconda (described below), which gives you access to the “Anaconda Prompt” desktop application. (Instructions for this are given on the Anaconda Website.)

From the Anaconda Prompt, you should be able to run conda and other shell commands.

Lightweight miniconda#

If you don’t want to download a large file like the Anaconda Python Distribution (ca. 800 MB), there is a lightweight alternative installation called miniconda.

Google Colab#

You can even start 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.

Managing environments with conda#

Python coupled with a package manager provides a way to make isolated, reproducible environments where you have fine-tuned control over all packages and configuration.

First, ensure that your conda installation is up to date:

conda update -n base -c conda-forge conda

To create a conda environment, you execute the following command:

conda create --name my_environment python=3.11 numpy

To use this environment, simply “activate” it by executing:

conda activate my_environment

You should now see the string (my_environment) prepended to your prompt. Now, if you execute any Python-related tool from the command line, it will first search in your environment.

To install additional packages into your environment:

conda install <package-name>

Some packages are community-maintained (e.g. conda-forge) and require you to specify a different “channel”:

conda install -c conda-forge <package-name>

You can deactivate your environment by typing:

conda deactivate

To see all the environments on your system:

conda info --envs

To get a complete summary of all the packages installed in your environment, run

conda list

If you want to permanently remove an environment and delete all the data associated with it:

conda env remove --name my_environment --all

A conda environment can also be defined through an environment.yaml file. With that file, a new environment with the exact configuration can be installed by executing

conda env create -f my_environment.yml

For extensive documentation on using environments, please see the conda documentation.

Environment for this course: esm-2024#

… with conda#

The latest environment specification for this course can be downloaded under the following link as a YAML-file:


There is a download button at the top-right corner.

After navigating to the folder where the environment.yaml file is stored, you can reate this environment using conda (faster)

conda env create -f environment.yaml

Activate this environment

conda activate esm-2024

This environment should be sufficient for all of your work in this course.

The environment has to be activated whenever you open a new terminal, before starting a new Jupyter window with

jupyter lab

… with pip#

If you want to use pip for managing your environment, download


There is a download button at the top-right corner.

After navigating to the folder where the requirements.txt file is stored, you can install the required packages with

pip install -r requirements.txt

This should allow you to start a new Jupyter window:

jupyter lab


JupyterLab will be our primary method for interacting with the computer. JupyterLab contains a complete environment for interactive data science which runs in your web browser.

JupyterLab has excellent documentation. Rather than repeat that documentation here, we point you to their docs. The following pages are particularly relevant:


Throughout the course, you might want to write rich text documents using Markdown. This is also very common in Jupyter Notebooks. Here are some useful references on Markdown syntax.