Machine Learning for Renewable Energy Systems
We’re offering the Seminar on Machine Learning for Renewable Energy Systems in the Summer Term 2025.
General
The seminar starts with a mandatory kick-off meeting and introductory lectures, followed by an individual working phase, where a meeting with your supervisor is mandatory. The presentations then take place in the second half of term.
The aim is that at the end of the course, you will:
- Understand the basics of renewable energy systems and the role machine learning can play to control, optimise, and understand them.
- Be able to identify current challenges in renewable energy systems.
- Be able to discuss machine learning approaches that address these challenges.
- Be able to implement and present a machine learning approach to address a challenge in renewable energy systems.
What you’ll have to do
- implement an ML method on given energy data and present your approach as a publicly available tutorial
- present your notebook to the group (15 mins)
Organisation and Evaluation
Credits: 3 ECTS
Language: English
Dates & Room: Tuesdays, 4 - 6pm, Lecture Hall, AI Research Building (Maria-von-Linden Str 6).
The final code notebook (75%) and the presentation (25%) are graded. Preliminary deadlines can be found at the bottom of this page in the tentative course plan.
Registration
If you want to participate in the course, attend the first meeting on Tuesday 15th of April, 4pm and register via Moodle.
Tentative Course Plan
Date | Topic |
---|---|
15.4. | Overview of the Seminar & Topic Introductions |
22.4. | Deadline to Decide on Topic and Commit to Seminar (no meeting) |
29.4. | Lecture: Introduction to Renewable Energy Systems |
06.5. | Lecture: Energy Markets and Energy System Modelling |
13.5. | Lecture: Climate Change Impacts and Sustainable ML |
14.5. - 30.6. | Individual Working Phase with mandatory meeting |
30.6. | Deadline Tutorial Notebook Hand-In |
01.7. | Presentations and Discussion |
08.7. | Presentations and Discussion |
15.7. | Presentations and Discussion |
22.7. | Presentations and Discussion (if needed) |