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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Florian Ebmeier

PhD Student

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Gwen Hirsch

MSc student

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Ludwig Bald

MSc Student

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Nicole Ludwig

Research Group Leader

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Nina Effenberger

PhD Student

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Ricarda Hogl

MSc Student

Alumni

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Kibidi Neocosmos

Student Research Assistant

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Thomas Kittel

Master Machine Learning

Research Areas

Climate Change Impacts on Energy Systems
How does climate change impact a renewable energy system?
Climate Change Impacts on Energy Systems
Demand Side Flexibility & Markets
How can we find standard patterns in energy time series to help size storage systems, or detect flexibility potentials?
Demand Side Flexibility & Markets
Probabilistic Energy Forecasting
How can we predict energy demand and supply, while assessing the uncertainty?
Probabilistic Energy Forecasting
Statistical, Physical Information and Modularity
How can we include statistical information such as seasonality, trends or concept drifts into DNNs to improve forecasting?
Statistical, Physical Information and Modularity

Where to find us

  • Maria-von-Linden Str 6
    72076 Tübingen
  • 4th floor, North Wing