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disaggregator

A set of tools for processing of spatial and temporal disaggregations of demands of electricity, heat and natural gas.
https://github.com/DemandRegioTeam/disaggregator

Category: Energy Systems
Sub Category: Load and Demand Forecasting

Keywords from Contributors

energy-system energy-system-model

Last synced: about 9 hours ago
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Repository metadata

A set of tools for processing of spatial and temporal disaggregations.

README.md

DemandRegio

This project aims at setting up both a database and a python toolkit called disaggregator for

  • temporal and
  • spatial disagregation

of demands of

  • electricity,
  • heat and
  • natural gas

of the final energy sectors

  • private households,
  • commerce, trade & services (CTS) and
  • industry.

Installation

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 disaggregator via

$ 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:

Jupyter_View

Results

Jupyter_View

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.

Jupyter_View

Acknowledgements

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)

License

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.

disaggregator is released as free software under the GPLv3, see LICENSE for further information.

GitHub Events

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Committers metadata

Last synced: 9 days ago

Total Commits: 258
Total Committers: 6
Avg Commits per committer: 43.0
Development Distribution Score (DDS): 0.593

Commits in past year: 0
Committers in past year: 0
Avg Commits per committer in past year: 0.0
Development Distribution Score (DDS) in past year: 0.0

Name Email Commits
Paul Verwiebe v****e@t****e 105
Fabian Gotzens f****s@f****e 89
Fabian Gotzens f****s@f****m 57
FRONTIER\anna.lane a****e@f****m 5
Fabian Neumann f****n@o****e 1
Uwe Krien u****t@p****u 1

Committer domains:

Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 14
Total pull requests: 7
Average time to close issues: about 1 month
Average time to close pull requests: about 1 month
Total issue authors: 8
Total pull request authors: 6
Average comments per issue: 1.5
Average comments per pull request: 1.0
Merged pull request: 2
Bot issues: 0
Bot pull requests: 0

Past year issues: 2
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 1
Past year pull request authors: 0
Past year average comments per issue: 2.5
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0

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Dependencies

environment.yml pypi

Score: 5.552959584921617