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.

Research Areas

Climate Change Impacts on Energy Systems

How does climate change impact a renewable energy system?

Climate Change Impacts on Energy Systems

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

Teaching

Seminar Summer Term 2022

We’re again offering the Seminar on Machine Learning for Renewable Energy Systems in the Summer Term 2022.

Last updated on Jun 13, 2022

Seminar Winter Term 21/22

The group offers a Seminar in the Winter Term 21/22 on Machine Learning for Renewable Energy Systems.

Last updated on Feb 17, 2022

Recent Publications

Jonathan Wenger, Nicholas Krämer, Marvin Pförtner, Jonathan Schmidt, Nathanael Bosch, Nina Effenberger, Johannes Zenn, Alexandra Gessner, Toni Karvonen, François-Xavier Briol, Maren Mahsereci, Philipp Hennig (2021). ProbNum: Probabilistic Numerics in Python.

Nina Effenberger, Nicole Ludwig (2021). A Collection and Categorization of Open-Source Wind and Wind Power Datasets.

Benedikt Heidrich, Andreas Bartschat, Marian Turowski, Oliver Neumann, Kaleb Phipps, Stefan Meisenbacher, Kai Schmieder, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer (2021). pyWATTS: Python Workflow Automation Tool for Time Series.

Kaleb Phipps, Sebastian Lerch, Maria Andersson, Ralf Mikut, Veit Hagenmeyer, Nicole Ludwig (2021). Evaluating Ensemble Post-Processing for Wind Power Forecasts.

Frederik vom Scheidt, Hana Medinová, Nicole Ludwig, Bent Richter, Philipp Staudt, Christof Weinhardt (2020). Data analytics in the electricity sector – A quantitative and qualitative literature review. Energy and AI.

See all publications

Where to find us

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