pyGRETA
Python Generator of REnewable Time series and mAps: a tool that generates high-resolution potential maps and time series for user-defined regions within the globe.
https://github.com/tum-ens/pygreta
Category: Energy Systems
Sub Category: Renewable Energy Integration
Keywords
csp gis high-resolution potentials pv renewable-energy renewable-timeseries wind
Keywords from Contributors
energy-system grid rasters regions preprocessing simulator
Last synced: about 1 hour ago
JSON representation
Repository metadata
python Generator of REnewable Time series and mAps
- Host: GitHub
- URL: https://github.com/tum-ens/pygreta
- Owner: tum-ens
- License: gpl-3.0
- Created: 2019-03-08T17:08:33.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-04-20T09:45:25.000Z (about 3 years ago)
- Last Synced: 2025-04-25T13:03:50.415Z (2 days ago)
- Topics: csp, gis, high-resolution, potentials, pv, renewable-energy, renewable-timeseries, wind
- Language: Python
- Homepage:
- Size: 224 MB
- Stars: 43
- Watchers: 5
- Forks: 15
- Open Issues: 11
- Releases: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
python Generator of REnewable Time series and mAps: a tool that generates high-resolution potential maps and time series for user-defined regions within the globe.
Features
- Generation of potential maps and time series for user-defined regions within the globe
- Modeled technologies: onshore wind, offshore wind, PV, CSP (user-defined technology characteristics)
- Use of MERRA-2 reanalysis data, with the option to detect and correct outliers
- High resolution potential taking into account the land use suitability/availability, topography, bathymetry, slope, distance to urban areas, etc.
- Statistical reports with summaries (available area, maximum capacity, maximum energy output, etc.) for each user-defined region
- Generation of several time series for each technology and region, based on user's preferences
- Possibility to combine the time series into one using linear regression to match given full-load hours and temporal fluctuations
Applications
This code is useful if:
- You want to estimate the theoretical and/or technical potential of an area, which you can define through a shapefile
- You want to obtain high resolution maps
- You want to define your own technology characteristics
- You want to generate time series for an area after excluding parts of it that are not suitable for renewable power plants
- You want to generate multiple time series for the same area (best site, upper 10%, median, lower 25%, etc.)
- You want to match historical capacity factors of countries from the IRENA database
You do not need to use the code (but you can) if:
- You do not need to exclude unsuitable areas - use the Global Solar Atlas or Global Wind Atlas
- You only need time series for specific points - use other webtools such as Renewables.ninja
- You only need time series for administrative divisions (countries, NUTS-2, etc.), for which such data is readily available - see Renewables.ninja or EMHIRES
Outputs
Potential maps for solar PV and onshore wind in Australia, using weather data for 2015:
Contributors ✨
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!
Please cite as:
Kais Siala, & Houssame Houmy. (2020, June 1). tum-ens/pyGRETA: python Generator of REnewable Time series and mAps (Version v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.3727416
Owner metadata
- Name: Chair of Renewable and Sustainable Energy Systems
- Login: tum-ens
- Email:
- Kind: organization
- Description:
- Website: http://www.ens.ei.tum.de
- Location: Technical University of Munich
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/8157454?v=4
- Repositories: 18
- Last ynced at: 2024-03-18T00:21:03.075Z
- Profile URL: https://github.com/tum-ens
GitHub Events
Total
- Watch event: 3
- Fork event: 1
Last Year
- Watch event: 3
- Fork event: 1
Committers metadata
Last synced: 7 days ago
Total Commits: 661
Total Committers: 7
Avg Commits per committer: 94.429
Development Distribution Score (DDS): 0.53
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 | Commits | |
|---|---|---|
| HoussameH | h****h@g****m | 311 |
| kais-siala | k****a@t****e | 226 |
| Patrick Buchenberg | p****g@t****e | 76 |
| thushara2020 | 7****0 | 25 |
| allcontributors[bot] | 4****] | 20 |
| Siala | g****x@m****e | 2 |
| sonercandas | s****s@t****e | 1 |
Committer domains:
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 62
Total pull requests: 128
Average time to close issues: 3 months
Average time to close pull requests: 3 days
Total issue authors: 7
Total pull request authors: 7
Average comments per issue: 1.02
Average comments per pull request: 0.16
Merged pull request: 114
Bot issues: 0
Bot pull requests: 10
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
Top Issue Authors
- kais-siala (39)
- HoussameH (12)
- simnh (6)
- SaberaAli (2)
- filljonas (1)
- TommasoPillon (1)
- denis-bz (1)
Top Pull Request Authors
- kais-siala (64)
- HoussameH (49)
- allcontributors[bot] (10)
- patrick-buchenberg (2)
- pgrimaud (1)
- raaphy (1)
- lodersky (1)
Top Issue Labels
- enhancement (23)
- bug (5)
- question (5)
- help wanted (4)
- lowpriority (2)
- wontfix (1)
- invalid (1)
- good first issue (1)
Top Pull Request Labels
- enhancement (2)
- bug (1)
Score: 5.934894195619588