Data-driven understanding of energy systems
Benjamin Schäfer
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 an Assistant Professor, leading a group at the Karlsruhe Institute of Technology (KIT) on Data-driven analysis of complex systems (DRACOS).
Research
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.
Publications
Recent highlights
During the ACM e-energy 2024, my team and I publushed articles on the forecasting of highly-resolved time series (ACM e-energy 2024 Pütz et al) and on how we can identify complex power grid dynamics purely from data (ACM e-energy 2024 Wen et al)
Combining domain knowledge, we derived stochastic forecasts and deeper insights into the complex dynamics of power grid frequency trajectories PRX Energy 2023
Using explainable artificial intelligence, we obtained insights into how electricity prices depend on external factors Energy and AI 2023 as well as how regulatory changes impact price dynamics ACM e-energy 2023
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 (I also published a German public science article in Spektrum)
Contact
If any of this sounds interesting to you, feel free to get in touch