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
Nicole Ludwig
Research Group Leader
Florian Ebmeier
PhD Student
Gwen Hirsch
PhD Student
Kibidi Neocosmos
PhD Student
Kornelius Raeth
PhD Student
Nina Effenberger
PhD Student
Mara Seyfert
MSc Student
Research Areas
How does climate change impact a renewable energy system?
How can we assess and improve the sustainability of machine learning models?
How can we reliably predict time series in complex systems under uncertainty?
How can we include statistical information such as seasonality, trends or concept drifts into DNNs to improve forecasting?
How can we recognise, promote and use flexibility on the demand side?
BMBF funded research project: Machine Learning in Complex Systems under Uncertainty
Teaching
Recent Publications
(2022).
Probabilistic load forecasting using post-processed weather ensemble predictions.
Journal of the Operational Research Society.
(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
