Open Energy Tracker
An open data platform for monitoring and visualizing energy policy targets.
https://gitlab.com/diw-evu/oet/openenergytracker
Category: Energy Systems
Sub Category: Energy Policy
Keywords
data visualization dataset energy plotly policy python renewable energy
Keywords from Contributors
GAMS open source model power market model energy-storage flexibility-options gams open-source-modeling power-sector-modeling renewable-energy-integration sector-coupling
Last synced: about 22 hours ago
JSON representation
Repository metadata
The Open Energy Tracker is an open data platform for monitoring and visualizing energy policy targets.
- Host: gitlab.com
- URL: https://gitlab.com/diw-evu/oet/openenergytracker
- Owner: diw-evu
- License: cc-by-4.0
- Created: 2021-11-29T16:20:14.107Z (over 3 years ago)
- Default Branch: main
- Last Synced: 2025-04-20T09:04:16.909Z (7 days ago)
- Topics: data visualization, dataset, energy, plotly, policy, python, renewable energy
- Stars: 7
- Forks: 5
- Open Issues: 0
- Releases: 0
https://gitlab.com/diw-evu/oet/openenergytracker/blob/main/
# Open Energy Tracker [](https://gitlab.com/diw-evu/openenergytracker/-/releases) [](https://gitlab.com/diw-evu/openenergytracker/-/commits/main) ## An open data platform for monitoring energy policy targets. With the [Open Energy Tracker](https://openenergytracker.org/en/), we provide an open data platform for monitoring and visualizing energy policy goals. Initially, the focus was on Germany and the goals of the current federal government. We want to extend this data platform to other countries, initially to selected European countries. France has already been included and further countries will follow. Beyond that, we are open to extensions of other countries and regions, also outside Europe, ideally collaboratively with respective regional experts. We graphically depict governmental targets, most of which are specified for the year 2030, and compare them with the current status. We focus on renewable energy expansion, electric mobility and electrolysis, but cover also other indicators. All figures, explanations, and links to the underlying data can be found on the [main page of the project](https://openenergytracker.org/de/). ## "Ampel-Monitor Energiewende", formerly known as "KoaVTracker": monitoring energy goals of the new German government In their [coalition agreement](https://www.spd.de/fileadmin/Dokumente/Koalitionsvertrag/Koalitionsvertrag_2021-2025.pdf) of 24.11.2021, the coalition parties of the new German government set themselves various quantitative targets in the energy sector. We have been pursuing these since the end of 2021, initially under the name **"KoaVTracker "**. Since then, however, the federal government has increasingly set itself further targets that go beyond the coalition agreement, so we have decided to rename our tool. It is now called **"Ampel-Monitor Energiewende "**. From July 2022, this traffic light monitor will also have its own [DIW Berlin website](https://www.diw.de/ampel-monitor) and regular publications. The complete set indicators will, including all countries, is available on the [homepage of the Open Energy Tracker](https://openenergytracker.org/en/). ## About us The Open Energy Tracker was developed by [Wolf-Peter Schill](https://www.diw.de/cv/en/wschill), [Alexander Roth](https://www.diw.de/cv/en/aroth), [Adeline Guéret](https://www.diw.de/cv/en/agueret), and [Felix Schmidt](https://www.diw.de/cv/en/fschmidt) of the [Transformation of the Energy Economy](https://twitter.com/transenerecon) research unit of the [Energy, Transport, Environment](https://www.diw.de/de/diw_01.c.604205.de/abteilung_energie__verkehr__umwelt.html) department at [DIW Berlin](https://www.diw.de/de). In particular, the parts of the tracker in which we report as "Ampel-Monitor Energiewende" were created in the context of the BMBF-funded Copernicus project [Ariadne](https://ariadneprojekt.de/). ## Contact Please see the [about](https://openenergytracker.org/en/docs/about/) page on the Open Energy Tracker.
Owner metadata
- Name: DIW EVU
- Login: diw-evu
- Email:
- Kind: organization
- Description:
- Website:
- Location:
- Twitter:
- Company:
- Icon url: https://gitlab.com/uploads/-/system/group/avatar/6612146/diw.jpg
- Repositories: 16
- Last ynced at: 2023-03-03T10:51:53.011Z
- Profile URL: https://gitlab.com/diw-evu
Committers metadata
Last synced: 6 months ago
Total Commits: 1,900
Total Committers: 11
Avg Commits per committer: 172.727
Development Distribution Score (DDS): 0.628
Commits in past year: 437
Committers in past year: 8
Avg Commits per committer in past year: 54.625
Development Distribution Score (DDS) in past year: 0.739
Name | Commits | |
---|---|---|
Alexander Roth | a****h@d****e | 706 |
Wolf-Peter Schill | w****l@d****e | 605 |
Adeline Guéret | a****t@g****m | 315 |
Nico Aichner | n****r@d****e | 114 |
fschmidt | f****t@d****e | 75 |
Lars Felder | l****r@d****e | 43 |
kelvin-say | k****y@g****m | 32 |
Jan Czimmeck | j****k@d****e | 4 |
alefunxo | a****o@e****h | 3 |
Martin Kittel | m****l@d****e | 2 |
soniaich | n****r@w****e | 1 |
Committer domains:
Issue and Pull Request metadata
Last synced: 6 months ago
Total issues: 0
Total pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Total issue authors: 0
Total pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull request: 0
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
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: 0
Past year pull request authors: 0
Past year average comments per issue: 0
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
- kaleido *
- mkdocs >=1.1.2
- mkdocs-git-revision-date-localized-plugin *
- mkdocs-git-revision-date-plugin *
- mkdocs-material >=5.4.0
- mkdocs-redirects *
- mkdocs-same-dir *
- mkdocs-static-i18n *
- openpyxl *
- pandas *
- path *
- plotly *
- sklearn *
Score: 4.343805421853684