This project aims at setting up both a database and a python toolkit called
- temporal and
- spatial disagregation
of demands of
- heat and
- natural gas
of the final energy sectors
- private households,
- commerce, trade & services (CTS) and
Before we really start, please install
conda through the latest Anaconda package or via miniconda. After successfully installing
conda, open the Anaconda Powershell Prompt.
For experts: You can also open a bash shell (Linux) or command prompt (Windows), but then make sure that your local environment variable
PATH points to your anaconda installation directory.
Now, in the root folder of the project create an environment to work in that will be called
$ conda env create -f environment.yml
which installs all required packages. Then activate the environment
$ conda activate disaggregator
How to start
Once the environment is activated, you can start a Jupyter Notebook from there
(disaggregator) $ jupyter notebook
As soon as the Jupyter Notebook opens in your browser, click on the
01_Demo_data-and-config.ipynb file to start with a demonstration:
How does it work?
For each of the three sectors 'private households', 'commerce, trade & services' and 'industry' the spatial and temporal disaggregation is accomplished through application of various functions. These functions take input data from a database and return the desired output as shwon in the diagram. There are four Demo-Notebooks to present these functions and demonstrate their execution.
The development of disaggregator was part of the joint DemandRegio-Project which was carried out by
- Forschungszentrum Jülich GmbH (Simon Burges, Bastian Gillessen, Fabian Gotzens)
- Forschungsstelle für Energiewirtschaft e.V. (Tobias Schmid)
- Technical University of Berlin (Stephan Seim, Paul Verwiebe)
Current version of software written and maintained by Paul A. Verwiebe (TUB)
Original version of software written by Fabian P. Gotzens (FZJ), Paul A. Verwiebe (TUB), Maike Held (TUB), 2019/20.