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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Ludwig Bald

MSc Student

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Nicole Ludwig

Early Career 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

Probabilistic Energy Forecasting
How can we predict energy demand and supply, while assessing the uncertainty?
Probabilistic Energy Forecasting
Statistical Information and Data Representation in Deep Neural Networks
How can we include statistical information such as seasonality, trends or concept drifts into DNNs to improve forecasting?
Statistical Information and Data Representation in Deep Neural Networks
Pattern Recognition in Energy Time Series
How can we find standard patterns in energy time series to help size storage systems, or detect flexibility potentials?
Pattern Recognition in Energy Time Series

Where to find us

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