Course Materials for Data Science for Energy System Modelling

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!

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

Didn’t record the lectures this year.