Course Materials for Data Science for Energy System Modelling

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This semester I taught a new course at TU Berlin about Data Science for Energy System Modelling, for which I built a website with energy-focused Python tutorials:

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

It includes hands-on introductions to various libraries useful for modelling energy systems and processing data: 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, prices load)
  • tabular geographical data (e.g. location of power plants, LNG terminals, industrial sites)
  • converting reanalysis weather data to renewable generation (e.g. ERA5)
  • land eligibility analysis (e.g. where to build wind turbines)
  • working with maps and projections
  • optimisation modelling
  • electricity market modelling
  • networks & linearised power flow
  • capacity expansion planning
  • modelling sector-coupling
  • (interactive) visualisation/dashboards

I would also like to mention two resources that were incredibly helpful in getting this course started:

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Hi Fabian,

Are there any recordings/lecture slides the course at TU Berlin? Can we get access to them?

Thanks!

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You can use @nworbmot Energy Systems course alongside the website: teaching | nworbmot:tombrown

Didn’t record the lectures this year.

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This is great stuff Fabian, thanks a lot!

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To note the UK‑based Climate Compatible Growth (CCG) programme is offering online self‑study courses focusing on aspects of energy system modeling and related analysis:

The courses comprise lectures, quizes, and hands‑on exercises. Some courses are offered in Spanish as well as English. A certificate is provided upon completion.

Be aware that not all the tooling used in these courses is open‑source, so you may to need to do your own checks before signing on if software freedom is an important consideration.

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