Allgemeine Informationen

Veranstaltungsname	Vorlesung: Python for Wind Energy Systems
Untertitel
Semester	WiSe 2026
Aktuelle Anzahl der Teilnehmenden	0
Heimat-Einrichtung	FB2 Energy and Life Science
Veranstaltungstyp	Vorlesung in der Kategorie Lehre
Nächster Termin	Dienstag, 22.09.2026 14:00 - 17:15, Ort: (H302/303)
Art/Form

Lehrende

Dr. rer. nat. Andreas Heinen, Prof.

Räume und Zeiten

(H302/303): Dienstag: 14:00 - 17:15, wöchentlich (15x)

Studienbereiche

Kommentar/Beschreibung

Technical elective master course in the Master in Wind Energy Engineering (3rd Semester) and the Master in Sustainable Energy (EUF).

The course is a mixture of lectures and integrated exercises which provide learning opportunities for projects.

If you have any questions after reading the below, please get in touch!

If you do not have any experience with Python yet, I still recommend the course, IF you have some other programming knowledge in a different language (Matlab, C/C++, Java, R, ...).

The form of examination is a presentation and written report at the end of the semester based on a chosen topic. Group work with 3-4 people is ideal with a group of your own choice.

Main subjects covered:
• Modeling and analysis of weather parameters relevant for wind generation.
• Forecasting of volatile generation output, stochastic modeling.
• Wake modeling.
• Integration of wind assets in markets and the electricity system.
• Dynamics of short-term markets, forward/future markets and integrated modeling of volume and price risk.
• Balancing/reserve markets; UCTE grid and frequency; redispatch and curtailment of wind assets
• Various other Python based data analysis and modeling typically in a “Use-Modify-Create” method

Learning outcomes:
• Students can analyze specific challenges and opportunities for integrating volatile wind generation into the electricity system.
• Students learn to apply Python for advanced data analysis and visualization.
• Students learn techniques for analyzing volatile wind and weather parameters.
• Students learn techniques for forecasting and modeling volatile wind generation output and quantifying forecast uncertainty.
• Students learn to use advanced Python libraries for wind power modeling, e.g. for quantification of wake effects.
• Students learn to interpret and apply knowledge in the dynamics of electricity prices for strategic market participation.
• Students learn to implement quantitative models for forecasting, risk assessment, and market simulation using Python.
• Students can critically evaluate market trends and policy changes affecting the commercial viability of wind projects.

Vorlesung: Python for Wind Energy Systems - Details