
Data-driven analysis of complex systems (DRACOS)
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Helmholtz Young Investigator Group
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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?
Images adapted – Icon made by Becris from https://www.flaticon.com/
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 2.0 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.
Portrait | Name | Function/Research Interests |
---|---|---|
Hallah Shahid Butt | PhD Student (Explainable Reinforcement Learning) | |
Hadeer Ahmed Hamed El Ashhab | PhD Student (Deep Learning and Forecasting) | |
Ulrich Jakob Oberhofer, M.Sc. | PhD student (Stochastic Modelling, Physics-inspired Machine Learning) | |
Sebastian Pütz, M.Sc. | PhD Student (Forecasting, Graph Neural Networks) | |
TT-Prof. Dr. Benjamin Schäfer | Assistant Professor (with tenure-track) | |
Xinyi Wen | PhD student (statistical inference, forecasting) |
Publications
Trebbien, J.; Rydin Gorjão, L.; Praktiknjo, A.; Schäfer, B.; Witthaut, D.
2023. Energy and AI, 13, Article no: 100250. doi:10.1016/j.egyai.2023.100250
Pütz, S.; Kruse, J.; Witthaut, D.; Hagenmeyer, V.; Schäfer, B.
2023. Companion Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy ’23), 26–31, Association for Computing Machinery (ACM). doi:10.1145/3599733.3600247
Schäfer, B.; Rydin Gorjão, L.; Yalcin, G. C.; Förstner, E.; Jumar, R.; Maass, H.; Kühnapfel, U.; Hagenmeyer, V.
2023. (H. Sayama, Ed.) Complexity, 2023, Art.-Nr.: 2657039. doi:10.1155/2023/2657039
Lund Onsaker, T.; Nygård, H. S.; Gomila, D.; Colet, P.; Mikut, R.; Jumar, R.; Maass, H.; Kühnapfel, U.; Hagenmeyer, V.; Schäfer, B.
2023. Journal of Physics: Complexity, 4 (1), Article no: 015012. doi:10.1088/2632-072X/acbd7f
Hertel, M.; Beichter, M.; Heidrich, B.; Neumann, O.; Ott, S. M.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2023. Helmholtz Artificial Intelligence Conference (Helmholtz AI 2023), Hamburg, Germany, June 12–14, 2023
He, H.; Schäfer, B.; Beck, C.
2022. Scientific Reports, 12 (1), Artikel-Nr.: 12215. doi:10.1038/s41598-022-16109-2
Schäfer, B.; Beck, C.; Rhys, H.; Soteriou, H.; Jennings, P.; Beechey, A.; Heppell, C. M.
2022. Scientific Reports, 12 (1), Art.-Nr.: 12346. doi:10.1038/s41598-022-16342-9
Kruse, J.; Schäfer, B.; Witthaut, D.
2022. Electric Power Systems Research, 212, Art.-Nr.: 108489. doi:10.1016/j.epsr.2022.108489
Lund Onsaker, T.; Nygård, H. S.; Gomila, D.; Colet, P.; Mikut, R.; Jumar, R.; Kühnapfel, U.; Maass, H.; Hagenmeyer, V.; Witthaut, D.; Schäfer, B.
2022, September 27. doi:10.48550/arXiv.2209.15414
Hertel, M.; Ott, S.; Neumann, O.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2022. 36. Conference on Neural Information Processing Systems (NeurIPS 2022), Online, November 28–December 10, 2023
Böttcher, P. C.; Rydin Gorjão, L.; Beck, C.; Jumar, R.; Maass, H.; Hagenmeyer, V.; Witthaut, D.; Schäfer, B.
2022. Energy Advances, 2 (1), 91–97. doi:10.1039/D2YA00150K
Hertel, M.; Ott, S.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.; Neumann, O.
2022. Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022. Hrsg.: H. Schulte, F. Hoffmann; R. Mikut, 93–110, KIT Scientific Publishing
Schäfer, B.; Pesch, T.; Manik, D.; Gollenstede, J.; Lin, G.; Beck, H.-P.; Witthaut, D.; Timme, M.
2022. doi:10.48550/arXiv.2209.13278
Schäfer, B.; Pesch, T.; Manik, D.; Gollenstede, J.; Lin, G.; Beck, H.-P.; Witthaut, D.; Timme, M.
2022. Nature Communications, 13 (1), Art.-Nr.: 5396. doi:10.1038/s41467-022-32917-6
Schmidt, R.; Haehne, H.; Hillmann, L.; Casadiego, J.; Witthaut, D.; Schafer, B.; Timme, M.
2022. IEEE Access, 10, 76682–76692. doi:10.1109/ACCESS.2022.3191665
Heidrich, B.; Mannsperger, L.; Turowski, M.; Phipps, K.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2022. DACH+ Conference on Energy Informatics German Federal Ministry for Economic Affairs and Energy
Heidrich, B.; Mannsperger, L.; Turowski, M.; Phipps, K.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2022. Energy Informatics, 5 (S1), Article no: 20. doi:10.1186/s42162-022-00214-7
Anvari, M.; Proedrou, E.; Schäfer, B.; Beck, C.; Kantz, H.; Timme, M.
2022. Nature Communications, 13 (1), Art.-Nr.: 4593. doi:10.1038/s41467-022-31942-9
Cramer, E.; Gorjao, L. R.; Mitsos, A.; Schafer, B.; Witthaut, D.; Dahmen, M.
2022. IEEE Access, 10, 8194–8207. doi:10.1109/ACCESS.2022.3141875
Tchuisseu, E. B. T.; Dongmo, E.-D.; Procházka, P.; Woafo, P.; Colet, P.; Schäfer, B.
2022. European Journal of Applied Mathematics, 34 (3), 467–483. doi:10.1017/S095679252100036X
Rydin Gorjao, L.; Vanfretti, L.; Witthaut, D.; Beck, C.; Schäfer, B.
2022. IEEE Access, 10, 18065–18073. doi:10.1109/ACCESS.2022.3150338
Rydin Gorjão, L.; Jumar, R.; Maass, H.; Hagenmeyer, V.; Yalcin, G. C.; Kruse, J.; Timme, M.; Beck, C.; Witthaut, D.; Schäfer, B.
2020. Nature Communications, 11 (1), Art.-Nr. 6362. doi:10.1038/s41467-020-19732-7
Kobitski, A. Y.; Otte, J. C.; Takamiya, M.; Schäfer, B.; Mertes, J.; Stegmaier, J.; Rastegar, S.; Rindone, F.; Hartmann, V.; Stotzka, R.; Garcia, A.; Wezel, J. van; Mikut, R.; Strähle, U.; Nienhaus, G. U.
2015. Scientific Reports, 5 (8601), 1–10. doi:10.1038/srep08601
Otte, J. C.; Kobitskiy, A.; Stegmaier, J.; Takamiya, M.; Hartmann, V.; Schäfer, B.; Garcia, A.; Wezel, J. van; Stotzka, R.; Nienhaus, G. U.; Mikut, R.; Strähle, U.
2013. Jahrestagung der Deutschen Gesellschaft für Experimentelle und Klinische Pharmakologie und Toxikologie (DGPT), Halle, 5.-7. März 2013