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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Samuel Wörz

MSc Student

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Tian Jin

MSc Student

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Tim Weisbarth

MSc Student

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.

Research Areas

Climate Change Impacts on Energy Systems

Climate Change Impacts on Energy Systems

How does climate change impact a renewable energy system?

Probabilistic Forecasting

Probabilistic Forecasting

How can we reliably predict time series in complex systems under uncertainty?

Statistical, Physical Information and Modularity

Statistical, Physical Information and Modularity

How can we include statistical information such as seasonality, trends or concept drifts into DNNs to improve forecasting?

Demand Side Flexibility & Markets

Demand Side Flexibility & Markets

How can we recognise, promote and use flexibility on the demand side?

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.

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

Where to find us

  • Maria-von-Linden Str 6
    72076 Tübingen