TT-Prof. Dr. Benjamin Schäfer

TT-Prof. Dr. Benjamin Schäfer

  • Karlsruhe Institute of Technology (KIT)
    Institute for Automation and Applied Informatics (IAI)
    Hermann-von-Helmholtz-Platz 1
    76344 Eggenstein-Leopoldshafen
    Fax: +49 721 608 22602
    Building-No.: 445 / 449 / 668

Link to personal website: https://benjaminschaefer.org/

 

KIT - STS - Medien/Public Relations - Presseservice - Expertinnen und Experten des KIT - Energie und KI – Tenure-Track-Professor Benjamin Schäfer (German only)

sprungmarken_marker_4645

Publications


2025
Probabilistic and Explainable Machine Learning for Tabular Power Grid Data
Nikoltchovska, A.; Pütz, S.; Li, X.; Hagenmeyer, V.; Schäfer, B.
2025. Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems, Rotterdam, 17th-20th June 2025, 213–231, Association for Computing Machinery (ACM). doi:10.1145/3679240.3734623
A Reinforcement Learning Framework for AI-Driven Control of Validated White-Box Building Models
Cheng, H.; Huang, Q.; Çakmak, H. K.; Schäfer, B.
2025, June. Helmholtz AI Conference: AI for Science (HAICON 2025), Karlsruhe, Germany, June 3–5, 2025
Analyzing deterministic and stochastic influences on the power grid frequency dynamics with explainable artificial intelligence
Drewnick, T.; Wen, X.; Oberhofer, U.; Rydin Gorjão, L.; Beck, C.; Hagenmeyer, V.; Schäfer, B.
2025. Chaos: An Interdisciplinary Journal of Nonlinear Science, 35 (3). doi:10.1063/5.0239371
Synthesizing Distribution Grid Congestion Data Using Multivariate Conditional Time Series Generative Adversarial Networks
Demirel, G.; Hauf, J.; Butt, H.; Förderer, K.; Schäfer, B.; Hagenmeyer, V.
2025. 2024 IEEE Sustainable Power and Energy Conference (iSPEC), 385–390, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/iSPEC59716.2024.10892479
Explainable time-series forecasting with sampling-free SHAP based on attention manipulation
Hertel, M.; Pütz, S.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2025. Helmholtz AI Conference: AI for Science (HAICON 2025), Karlsruhe, Germany, June 3–5, 2025
Spatial analysis of tails of air pollution PDFs in Europe
He, H.; Schäfer, B.; Beck, C.
2025. Environmental Data Science, 3, e30. doi:10.1017/eds.2024.43
2024
Focus on monitoring and control of complex supply systems
Delabays, R.; Pagnier, L.; Schäfer, B.; Tyloo, M.; Witthaut, D.
2024. Journal of Physics: Complexity, 5 (4), Article no: 040201. doi:10.1088/2632-072X/acfadd
Generating synthetic energy time series: A review
Turowski, M.; Heidrich, B.; Weingärtner, L.; Springer, L.; Phipps, K.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2024. Renewable and Sustainable Energy Reviews, 206, 114842. doi:10.1016/j.rser.2024.114842
Explainable Reinforcement Learning for Optimizing Electricity Costs in Building Energy Management
Butt, H. S.; Huang, Q.; Schäfer, B.
2024. 2024 3rd International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED), Limassol, Cyprus, 21-23 October 2024, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SyNERGYMED62435.2024.10799400
Identifying Complex Dynamics of Power Grid Frequency
Wen, X.; Oberhofer, U.; Gorjão, L. R.; Yalcin, G. C.; Hagenmeyer, V.; Schäfer, B.
2024. The 15th ACM International Conference on Future and Sustainable Energy Systems, 408–414, Association for Computing Machinery (ACM). doi:10.1145/3632775.3661944
Feasibility of Forecasting Highly Resolved Power Grid Frequency Utilizing Temporal Fusion Transformers
Pütz, S.; El Ashhab, H.; Hertel, M.; Mikut, R.; Götz, M.; Hagenmeyer, V.; Schäfer, B.
2024. e-Energy ’24: Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems, 447–453, Association for Computing Machinery (ACM). doi:10.1145/3632775.3661963
„Kein Anlass für Katastrophenszenarien“ – Mit KI Tools tragen KIT Forschende zur Stabilität in den grünen Stromnetzen der Zukunft bei - Campus-Report am 21.05.2024
Fuchs, S.; Schäfer, B.
2024. doi:10.5445/IR/1000170882
Why Reinforcement Learning in Energy Systems Needs Explanations
Butt, H. S.; Schafer, B.
2024. Proceedings of the 2024 Workshop on Explainability Engineering, 26–30, Association for Computing Machinery (ACM). doi:10.1145/3648505.3648510
Can SINDy identify power grid frequency dynamics?
Wen, X.; Oberhofer, U.; Rydin Gorjao, L.; Yalcin, G. C.; Hagenmeyer, V.; Schäfer, B.
2024. Workshop: Tackling Climate Change with Machine Learning (ICLR 2024)
Analyzing spatio-temporal dynamics of dissolved oxygen for the River Thames using superstatistical methods and machine learning
He, H.; Boehringer, T.; Schäfer, B.; Heppell, K.; Beck, C.
2024. Scientific Reports, 14 (1), Art.-Nr.: 21288. doi:10.1038/s41598-024-72084-w
Analysing and Predicting Extreme Frequency Deviations: A Case Study in the Balearic Power Grid
Sharfeddine, Z.; Pütz, S.; Tamhane, V.; Hagenmeyer, V.; Schäfer, B.
2024. Energy Informatics Review Newsletter: EIR Newsletter
Explainability and Benchmarking of Transformers for Time-Series Forecasting
Hertel, M.; Pütz, S.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2024. Helmholtz Artificial Intelligence Conference (Helmholtz AI 2024), Düsseldorf, Germany, June 12–14, 2024
2023
Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs
Trebbien, J.; Pütz, S.; Schäfer, B.; Nygård, H. S.; Gorjão, L. R.; Witthaut, D.
2023. Powering solutions for decarbonized and resilient future smartgrids, 5 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISGTEUROPE56780.2023.10407112
Physics-Informed Machine Learning for Power Grid Frequency Modeling
Kruse, J.; Cramer, E.; Schäfer, B.; Witthaut, D.
2023. PRX energy, 2 (4), Art.-Nr.: 043003. doi:10.1103/PRXEnergy.2.043003
Non-linear, bivariate stochastic modelling of power-grid frequency applied to islands
Oberhofer, U.; Gorjao, L. R.; Yalcin, G. C.; Kamps, O.; Hagenmeyer, V.; Schäfer, B.
2023. 2023 IEEE Belgrade PowerTech, 1, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/PowerTech55446.2023.10202986
Local versus global stability in dynamical systems with consecutive Hopf bifurcations
Böttcher, P. C.; Schäfer, B.; Kettemann, S.; Agert, C.; Witthaut, D.
2023. Physical Review Research, 5 (3), Art-Nr.: 033139. doi:10.1103/PhysRevResearch.5.033139
Understanding electricity prices beyond the merit order principle using explainable AI
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
Forecasting Power Grid Frequency Trajectories with Structured State Space Models
Pütz, S.; Schäfer, B.
2023, June 28. 14th ACM International Conference on Future Energy Systems (e-Energy 2023), Orlando, FL, USA, June 20–23, 2023. doi:10.1145/3599733.3606298
Regulatory Changes in German and Austrian Power Systems Explored with Explainable Artificial Intelligence
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
Microscopic Fluctuations in Power-Grid Frequency Recordings at the Subsecond Scale
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
Predicting the power grid frequency of European islands
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
Transformer training strategies for forecasting multiple load time series
Hertel, M.; Beichter, M.; Heidrich, B.; Neumann, O.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2023. Energy Informatics, 6 (S1), Art.-Nr.: 20. doi:10.1186/s42162-023-00278-z
Electrical Load Forecasting with Transformer Neural Networks
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
2022
Spatial heterogeneity of air pollution statistics in Europe
He, H.; Schäfer, B.; Beck, C.
2022. Scientific Reports, 12 (1), Artikel-Nr.: 12215. doi:10.1038/s41598-022-16109-2
Machine learning approach towards explaining water quality dynamics in an urbanised river
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
Secondary control activation analysed and predicted with explainable AI
Kruse, J.; Schäfer, B.; Witthaut, D.
2022. Electric Power Systems Research, 212, Art.-Nr.: 108489. doi:10.1016/j.epsr.2022.108489
Predicting the power grid frequency of European islands
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
Transformer Neural Networks for Building Load Forecasting
Hertel, M.; Ott, S.; Neumann, O.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2022. 36th Annual Conference on Neural Information Processing Systems (NIPS 2022), Online, November 28–December 9, 2022
Initial analysis of the impact of the Ukrainian power grid synchronization with Continental Europe
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
Evaluation of Transformer Architectures for Electrical Load Time-Series Forecasting
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
Understanding Braess’ Paradox in power grids
Schäfer, B.; Pesch, T.; Manik, D.; Gollenstede, J.; Lin, G.; Beck, H.-P.; Witthaut, D.; Timme, M.
2022. doi:10.48550/arXiv.2209.13278
Understanding Braess’ Paradox in power grids
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
Inferring Topology of Networks With Hidden Dynamic Variables
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
Boost short-term load forecasts with synthetic data from transferred latent space information
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
Boost short-term load forecasts with synthetic data from transferred latent space information
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
Data-driven load profiles and the dynamics of residential electricity consumption
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
Validation Methods for Energy Time Series Scenarios From Deep Generative Models
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
Secondary frequency control stabilising voltage dynamics
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
Phase and Amplitude Synchronization in Power-Grid Frequency Fluctuations in the Nordic Grid
Rydin Gorjao, L.; Vanfretti, L.; Witthaut, D.; Beck, C.; Schäfer, B.
2022. IEEE Access, 10, 18065–18073. doi:10.1109/ACCESS.2022.3150338
2020
Open database analysis of scaling and spatio-temporal properties of power grid frequencies
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