Data-driven analysis of complex systems
Understanding complex systems to support a sustainable future
Benjamin Schäfer is leading an Helmholtz AI young investigator group at Karlsruhe Institute of Technology ((KIT) and investigates how data science can facilitate the energy transition.
Mitigation of climate change requires a fundamental transformation of our energy system. Power plants based on fossil fuels must be replaced by renewable power sources, such as wind and solar power. This energy transition (“Energiewende”) towards a sustainable energy system raises numerous complex challenges, as power generation becomes more uncertain, while simultaneously more operational data becomes available. Hence, data-driven approaches become feasible and even necessary to fully understand the energy systems of today and tomorrow across all scales.
Machine learning and artificial intelligence can handle these enormous amounts of data but need to do so in a transparent way. Obtaining classifications or forecasts without explanations limits their use severely. Hence, the group aims to explain “black box” models using interpretability tools or develop outright interpretable models. For example, an algorithm should explain which external factors, such as the feed-in of photovoltaic systems, the current electricity price or the time of day, are relevant for its prediction of the power grid frequency or household consumption. This transparency then enables synergies from machine and human models: Where is the machine better than the human? What can we learn from this for our human models and thus make them better?
- Hallah Shahid Butt, PhD Student
- Hadeer Ahmed Hamed El Ashhab, PhD Student
- Ulrich Jakob Oberhofer, PhD Student
- Sebastian Pütz, PhD Student
- Xinyi Wen, PhD Student