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
Eric Stefan Miele
PhD student
Florian Ebmeier
PhD Student
Gwen Hirsch
MSc student
Ludwig Bald
MSc Student
Nicole Ludwig
Research Group Leader
Nina Effenberger
PhD Student
Ricarda Hogl
MSc Student
Alumni
Kibidi Neocosmos
Student Research Assistant
Thomas Kittel
Master Machine Learning
Research Areas
How does climate change impact a renewable energy system?
How can we find standard patterns in energy time series to help size storage systems, or detect flexibility potentials?
How can we predict energy demand and supply, while assessing the uncertainty?
How can we include statistical information such as seasonality, trends or concept drifts into DNNs to improve forecasting?
Recent Publications
(2022).
Adaptively coping with concept drifts in energy time series forecasting using profiles.
Proceedings of the Thirteenth ACM International Conference on Future Energy Systems.
(2022).
Analytical uncertainty propagation and storage usage in a high RES Turkish transmission grid scenario.
Proceedings of the Thirteenth ACM International Conference on Future Energy Systems.
Where to find us
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Maria-von-Linden Str 6
72076 Tübingen - 4th floor, North Wing
