Machine Learning in Sustainable Energy Systems
Dr. Nicole Ludwig is head of the Independent Research Group 'Machine Learning in Sustainable Energy Systems (MLSES)' at our Cluster of Excellence 'Machine Learning' at the University of Tübingen.
The MLSES group is interested in developing new machine learning algorithms to build and maintain a future sustainable energy system, which is intelligent and relies on as many renewable energy sources as is economically feasible and supported by society.
With climate and weather influencing renewable energy systems and their intermittent power supply, assessing uncertainty becomes more critical. We therefore seek to understand how uncertainty affects choices in future sustainable energy systems, as well as how to properly assess uncertainty to help make better decisions in these systems. We focus primarily on probabilistic machine learning methods for time series data, including forecasting and reinforcement learning with uncertain input.
For further information visit Nicole Ludwig's Lab Website or her Twitter channel
Before coming to Tübingen, Nicole Ludwig has been part of the DFG Research Training Group on "Energystatusdata" (2016 - 2019) and worked at the Institute for Automation and Applied Informatics (2019 - 2020), both at Karlsruhe Institute of Technology. She has written her PhD thesis in informatics on data-driven methods for demand-side flexibility in energy systems, supervised by Veit Hagenmeyer and has been a visiting researcher at the Mathematical Institute of the University of Oxford working with Siddharth Arora and James W. Taylor. Nicole Ludwig's research focuses on probabilistic machine learning seeking to understand the role of uncertainty in future sustainable energy systems. Her work has been awarded several best paper awards from leading conferences in energy informatics.
Dr. Nicole Ludwig
Machine Learning in Sustainable Energy Systems (MLSES)
Cluster of Excellence 'Machine Learning'
Maria-von-Linden-Str. 6, 4th floor
Room No. 40-31/A6
+49 7071 2970912