climatemind-backend
PyPSA
| climatemind-backend | PyPSA | |
|---|---|---|
| 1 | 2 | |
| 17 | 1,105 | |
| - | 2.9% | |
| 4.8 | 9.5 | |
| about 1 month ago | 3 days ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
climatemind-backend
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Looking for Python developers of any level to contribute to a ready-to-use Climate Action app
Our goal is to find a task for every developer who wants to contribute. To be able to do so we are trying to use the best open-source practices and developing a contributor guide. To start please follow the contributor guide. If you have any questions don't hesitate to ask questions on GitHub discussions page or by email [email protected]. The onboarding process and contributor guide are still in progress so we would like to hear from you any feedback about this.
PyPSA
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Young Australians Just Won a Human Rights Case Against an Enormous Coal Mine
This study about the European grid quantifies all this. If you want to go deeper into how they get these numbers, you'll need to read about PyPSA or other grid modelling tools. It's all open source and well documented.
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Solar panels set to be mandatory on all new buildings under EU plan New proposal aims to rapidly reduce dependence on Russian fossil and supercharge Europe’s transition to green energy
You need to know how energy models work, and I invite you to read about them. Check out PyPSA for instance (paper, Source code)). That would answer both points above and more. For instance, you need to know that they are based on real weather and consumption data, so they account for natural variability. They know the cost of producing synfuels, and they optimize around it to find the cheapest system overall given the input technologies and assumptions. These calculations would be impossible to do by hand.
What are some alternatives?
hass-kumo - Home Assistant module interfacing with Mitsubishi mini-split units
open-grid-emissions - Tools for producing high-quality hourly generation and emissions data for U.S. electric grids
pylandtemp - Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python.
snl-quest - An open source, Python-based software platform for energy storage simulation and analysis developed by Sandia National Laboratories.
meteostat-python - Access and analyze historical weather and climate data with Python.
co2-data - Data on CO2 and greenhouse gas emissions by Our World in Data
climate-change-assignment - A website explaining what is climate change
clouddrift - CloudDrift accelerates the use of Lagrangian data for atmospheric, oceanic, and climate sciences.
pypsa-eur - PyPSA-Eur: A Sector-Coupled Open Optimisation Model of the European Energy System
climate - 🌍🔥 Life Itself's ongoing inquiry into the climate crisis
gridstatus - Extract data from ISOs and other energy grid sources