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Overview
Our research aims to solve complex problems of our time, focusing on sustainability and especially the energy transition and the power grid. Methodologically, the group “Data-driven analysis of complex systems (DRACOS)” combines exploratory data analysis, physical modeling and machine learning methods. One focus here is on the interpretability of the models: Instead of “black box” predictions, we develop transparent models.
Research
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, we aim 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?
Our contributions are summarized in our vision and mission statements:
Vision: Enable a sustainable and collaborative future, where all energy generation is sustainable, data informs decisions and models are understood by everyone.
Mission: We develop and apply state-of-the-art machine learning methods to understand complex energy systems from households to transmission systems. We emphasize the openness of data and interpretability of models to enable collaboration among scientists and industry. We engage the general public by sharing and explaining our results.
Applications and partner
In our research, we utilize data from the Energy Lab and frequency measurements of various power grids. We collaborate with colleagues at KIT, other universities and Helmholtz centers, e.g. within Helmholtz AI (Helmholtz Artificial Intelligence Cooperation Unit).
For further information, contact Benjamin Schäfer.
| Name | Function/Research Interests | |
|---|---|---|
| Martin Sadric, M.Sc. | Research Associate (PhD Student) | |
| Dr. Qiong Huang | Postdoctoral Researcher (Reinforcement Learning, Multi-agent Systems) | |
| Dr. Xiao Li | Postdoc position (Koopman operator for power systems) | |
| Xinyi Wen | PhD student (statistical inference, forecasting) | |
| Ulrich Jakob Oberhofer, M.Sc. | PhD student (Stochastic Modelling, Physics-inspired Machine Learning) | |
| Sebastian Pütz, M.Sc. | PhD Student (Forecasting, Graph Neural Networks) | |
| Hallah Shahid Butt | PhD Student (Explainable Reinforcement Learning) | |
| Hadeer Ahmed Hamed El Ashhab | PhD Student (Deep Learning and Forecasting) | |
| Alexandra Nikoltchovska, M.Sc. | PhD Student | |
| TT-Prof. Dr. Benjamin Schäfer | Assistant Professor (with tenure-track) |
