Real-E: A Foundation Benchmark for Advancing Robust and Generalizable Electricity Forecasting
Shao, C.; Färber, M.; Pütz, S.; Schäfer, B.; Wang, Y.; Käfer, T.; Huang, Z.; Zhu, Z.
2025. CIKM ’25: The 34th ACM International Conference on Information and Knowledge Management Seoul Republic of Korea November 10 - 14, 2025, 6523–6527, Association for Computing Machinery (ACM). doi:10.1145/3746252.3761637

Deep Learning Approaches for the Detection of Ramping Artifacts in Schedule Deviation Data in Belgium
von Hülsen, H.; Oberhofer, U.; Lauwers, O.; Verbruggen, G.; Pütz, S.; Hagenmeyer, V.; Schäfer, B.
2025. 2025 IEEE Kiel PowerTech, 29 June 2025 - 03 July 2025, 1–8, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/PowerTech59965.2025.11180720

Nonlinear Stochastic Modeling of the South Korean Power Grid Frequency Dynamics
Oberhofer, U.; Wen, X.; Lee, J.; Kim, H.; Hagenmeyer, V.; Schäfer, B.
2025. 2025 IEEE Kiel PowerTech, June 29th – July 3rd, 2025. Kiel, 1–6, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/PowerTech59965.2025.11180372

Learning Frequency Dynamics of the Power System Using Higher-order Dynamic Mode Decomposition
Li, X.; Wen, X.; Schäfer, B.
2025. 2025 IEEE Kiel PowerTech, Kiel, 29th June - 3rd July 2025, 1–6, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/PowerTech59965.2025.11180213

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

BUILDA: A Thermal Building Data Generation Framework for Transfer Learning
Krug, T.; Raisch, F.; Aimer, D.; Wirnsberger, M.; Sigg, F.; Schäfer, B.; Tischler, B.
2025. arxiv. doi:10.48550/arXiv.2508.12703

A review of query systems for temporal n-gram corpora
Richter, F.; Schäfer, B.; Böhm, K.
2025. SCOLIA 2025. Scholarly Information Access 2025 : Proceedings of the First International Workshop on Scholarly Information Access co-located with 47th European Conference on Information Retrieval (ECIR 2025), Lucca, Italy, April 10th, 2025. Ed.: I. Frommholz, 18–31, CEUR-WS

Nonstandard power grid frequency statistics across continents
Wen, X.; Anvari, M.; Rydin Gorjão, L.; Yalcin, G. C.; Hagenmeyer, V.; Schäfer, B.
2025. Scientific Reports, 15 (1), 38470. doi:10.1038/s41598-025-25334-4

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

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

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. ACM SIGEnergy Energy Informatics Review, 4 (4), 155–162. doi:10.1145/3717413.3717427

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

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

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

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