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Home Page: https://fneum.github.io/data-science-for-esm/intro.html

License: MIT License

Jupyter Notebook 99.89% TeX 0.11%
data-science energy energy-data energy-system-modelling

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data-science-for-esm's Issues

git and github workshop

Would it make sense to add a small and concise introduction to git and github?
This introduction aims to teach the git concept and the difference between a soft & hard fork. The learning would be especially important for people aiming to join the development e.g. PhDs/ postdocs/professionals/ others. We would refine and extend the course from here to reach the standard of your other offered courses.

Happy to get allocated to this if there is interest.

image

linopy workshop

Awesome notebooks! Is there interest to convert the pyomo notebook to a linopy version?
Happy to work on this if you think it's useful

update workshop 12 - sector coupling with multilink default

Since the PR: PyPSA/PyPSA#669, my feeling is that this code block is not necessary. I am happy to get assigned to this task and deal with it (as well as the Linopy workshop)

Some sector-coupling technologies have multiple ouputs (e.g. CHP plants producing heat and power). By default, PyPSA links have only one input (bus0) and one output (bus1) with a given efficieny (efficiency). Thus, we have to tell PyPSA that links can have multiple outputs by overriding the component attributes.

override_component_attrs = pypsa.descriptors.Dict(
    {k: v.copy() for k, v in pypsa.components.component_attrs.items()}
)

override_component_attrs["Link"].loc["bus2"] = [
    "string",
    np.nan,
    np.nan,
    "2nd bus",
    "Input (optional)",
]
override_component_attrs["Link"].loc["efficiency2"] = [
    "static or series",
    "per unit",
    1.0,
    "2nd bus efficiency",
    "Input (optional)",
]
override_component_attrs["Link"].loc["p2"] = [
    "series",
    "MW",
    0.0,
    "2nd bus output",
    "Output",
]

Issue on page /03-workshop-pandas.html

Hi Fabian,

would it be possible to share the solutions for the Excercise Time Series Analysis? I'm currently struggling a bit with the task to fill up the NaN prices with data the week ahead.

Best regards,
Johannes

Improve Intro

Add to your intro what this website is about & what people will learn. Basically:
"This semester I taught a new course at TU Berlin on energy system modelling and data science, for which I built a small website with energy-focused Python tutorials. The course offers many hands-on introductions to various libraries that are useful for energy system modelling and processing data more generally. It includes tutorials and examples for getting started with Python, numpy, matplotlib, pandas, geopandas, cartopy, rasterio, atlite, networkx, pyomo, pypsa, plotly, hvplot, and streamlit. Topics covered include:

  • time series analysis (e.g. wind and solar production)
  • tabular data (e.g. LNG terminals)
  • geographical data (e.g. location of power plants)
  • data visualisation
  • converting weather data to renewable generation
  • land eligibility analysis (e.g. where to build wind turbines)
  • optimisation
  • electricity market modelling
  • power flow modelling (linearised)
  • capacity expansion planning
  • sector-coupling"
  • interactive visualisation and dashboarding"

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