Machine Learning in Sustainable Energy Systems

Who we are: We’re the MLSES group within the Cluster of Excellence – Machine Learning for Science at the University of Tübingen and are interested in developing new machine learning algorithms to build and maintain a future sustainable energy system.

Team

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Nicole Ludwig

Research Group Leader

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Florian Ebmeier

PhD Student

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Kibidi Neocosmos

PhD Student

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Kornelius Raeth

PhD Student

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Luca Schmidt

PhD Student

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Nina Effenberger

PhD Student

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Mara Seyfert

Research Fellow

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Sofia Morelli

Msc Student

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Tian Jin

MSc Student

Research Areas

Latest News

  • 15.03.2024: Kibidi won an Honorable Mention Award for his poster at the Machine Learning Summer School in Okinawa. Congrats Kibidi!
  • 16.11.2023: Our IN-ML-OUT project can now be seen at the Stadtmuseum Tübingen. For more details, follow the link
  • 10.11.2023: Nina's and Nicole's paper "Mind the (spectral) gap: How the temporal resolution of wind speed data affects multi-decadal wind power forecasts" got accepted! The paper is a result of a collaboration with Rachel White from UBC.

Teaching

Bachelor's and Master's Thesis/ Research Projects

If you want to write your thesis or research project within the group, please write an e-mail to Thesis/ Projects MLSES with the following information

  • current transcript of records
  • topics you are interested in
  • your ideal starting date

Any E-Mails not going to the specified address or not containing the above information will not be responded to.

We currently have some limited capacity for master thesis projects during the winter term.

Last updated on Oct 9, 2024
Time Series

In the winter term we’ll offer the lecture “Time Series” (6 ECTS).

Last updated on Oct 9, 2024
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Projects

IN-ML-OUT
Science Communication Project: Climate Change, Machine Learning and Wind Energy Forecasting
IN-ML-OUT
FEAT
BMBF funded research project: Machine Learning in Complex Systems under Uncertainty
FEAT

Recent Publications

Luca Schmidt, Nicole Ludwig (2024). Benchmarking Deep Learning Based Downscaling of Wind Speed.

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Kornelius Raeth, Nicole Ludwig (2024). Comparing Conditional Calibration of Aleatoric Uncertainty Estimators.

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Nina Effenberger, Marvin Pförtner, Philipp Hennig, Nicole Ludwig (2024). Probabilistic Wind Speed Downscaling for Future Wind Power Assessment.

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Florian Ebmeier, Nicole Ludwig, Jannik Thümmel, Georg Martius, Volker H. Franz (2024). Fault Detection in Solar Thermal Systems Using Probabilistic Reconstructions.

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Nina Effenberger, Nicole Ludwig, Rachel H. White (2023). Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts.

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Where to find us

  • Maria-von-Linden Str 6
    72076 Tübingen