🐍🧑🏫💻This semester I taught a new course about Data Science for Energy System Modelling, for which I built a website with energy-focused Python tutorials:
fneum.github.io/data-science-f…
@openmod @protontypes #energytwitter
fneum.github.io/data-science-f…
@openmod @protontypes #energytwitter
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)
- 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
- sector-coupling
- (interactive) visualisation/dashboards
- working with maps and projections
- optimisation modelling
- electricity market modelling
- networks & linearised power flow
- capacity expansion planning
- sector-coupling
- (interactive) visualisation/dashboards
The following resources were super helpful, especially for the first part of the course:
earth-env-data-science.github.io/intro.html
tomasbeuzen.com/python-program…
Thank you for your work! Big inspiration!
earth-env-data-science.github.io/intro.html
tomasbeuzen.com/python-program…
Thank you for your work! Big inspiration!
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