Data-driven understanding of energy systems
I am a Physicist who turned into a Data Scientist to contribute to our society by supporting the energy transition via data-driven approaches.
In my research, I investigate complex energy systems, using interpretable machine learning to understand, predict and design future energy systems.
Currently, I am leading a group at the Karlsruhe Institute of Technology (KIT) on Data-driven analysis of complex systems (DRACOS).
My goal is to understand complex energy systems from a data-driven point of view, i.e. without assuming too much prior knowledge.
I pay special attention to making any forecast or analysis transparent and explainable, using and developing interpretable machine learning tools. Thereby, I wish to identify drivers and risks for energy systems and help to design future systems, e.g. involving a high share of wind generation, electric cars, heat pumps etc.
Constructing new transmission lines in power grids may induce Braess' Paradox, i.e. reduce system stability. Our approach, identifies Braessian lines and may guide grid extension plans. Nature Communications 2022
Knowing the required electric power demand and its variations is necessary to balance demand and supply. We developed a data-driven approach to extract the trend and characterise demand fluctuations. Nature Communications 2022
Using explainable artificial intelligence, we identified drivers and risks for the stability of the power grid. Patterns 2021