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Benjamin Schäfer

Explainable time-series forecasting with sampling-free SHAP for Transformers
2026 · Matthias Hertel, Sebastian Pütz, et al. · Nature Communications · 10
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Data-Driven Load Profiles and the Dynamics of Residential Electric PowerConsumption
2020 · Anvari, Mehrnaz, Proedrou, Elisavet, et al. · arXiv (Cornell University) · 7
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Data and Code for "Initial analysis of the impact of the Ukrainian power grid synchronization with Continental Europe"
2022 · Böttcher, Philipp C., Gorjão, Leonardo Rydin, et al. · arXiv (Cornell University) · 2
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Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Study
2026 · Hadeer Elashhab, Sai Srijan Papineni, et al. · IEEE Access · 1
A highly configurable framework for large-scale thermal building data generation to drive machine learning research
2026 · Krug, Thomas, Raisch, Fabian, et al. · KITopen
On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models
2026 · Kreusel, Alexander, Beichter, Maximilian, et al. · arXiv (Cornell University)
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Qualifying Data Stewards in the Framework of Lifelong Learning. CAS FDM, Microcredentials and Innovative E-Learning Implementations
2026 · Habinger, Sophie Gabriele, Schäfer, Benjamin, et al. · Zenodo (CERN European Organization for Nuclear Research)
Transformer training strategies for forecasting multiple load time series
2026 · Hertel, Matthias, Beichter, Maximilian, et al. · Repository KITopen (Karlsruhe Institute of Technology)
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A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN
2026 · Degenkolb, Adrian, Huang, Qiong, et al. · arXiv (Cornell University)
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Graph Machine Learning: An Opportunity for Power Systems
2026 · Sadric, Martin, Pütz, Sebastian, et al. · arXiv (Cornell University)
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Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control
2026 · Zetto, Lukas, Schäfer, Benjamin, et al. · arXiv (Cornell University)
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Deep Learning Approaches for the Detection of Ramping Artifacts in Schedule Deviation Data in Belgium
2026 · Hülsen, Henrike von, Oberhofer, Ulrich, et al. · Repository KITopen (Karlsruhe Institute of Technology)
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High-fidelity inference of power grid frequency distributions
2026 · Lonardi, Alessandro, Schäfer, Benjamin, et al. · arXiv (Cornell University)
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BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control
2026 · Koch, Felix, Krug, Thomas, et al. · Repository KITopen (Karlsruhe Institute of Technology)
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A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods
2026 · Hertel, Matthias, Pütz, Sebastian, et al. · arXiv (Cornell University)
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Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting
2026 · Max Kleinebrahm, Jonathan Berrisch, et al. · International Conference on the European Energy Market (EEM)
Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting
2026 · Jan Niklas Lettner, Hadeer El Ashhab, et al. · ACM Sustainability Week
Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
2026 · Matthias Hertel, Alexandra Nikoltchovska, et al. · ACM International Conference on Future Energy Systems (e-Energy)
Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
2026 · Butt, Hallah Shahid, Huang, Qiong, et al. · arXiv (Cornell University)
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BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control
2026 · Koch, Felix, Krug, Thomas, et al. · arXiv (Cornell University)
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PowerSINDy: Identifying Nonlinear Time-Dependent Dynamics in Power Grid Frequency
2026 · Wen, Xinyi, Li, Xiao, et al. · arXiv (Cornell University)
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Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting
2026 · Kleinebrahm, Max, Berrisch, Jonathan, et al. · arXiv (Cornell University)
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Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting
2026 · Lettner, Jan Niklas, Ashhab, Hadeer El, et al. · arXiv (Cornell University)
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Predicting power grid frequency dynamics with invertible Koopman-based architectures
2026 · Lupascu, Eric, Li, Xiao, et al. · arXiv (Cornell University)
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Explainable time-series forecasting with sampling-free SHAP for Transformers
2026 · Hertel, Matthias, Pütz, Sebastian, et al. · Repository KITopen (Karlsruhe Institute of Technology)
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Works count: 29
Citation count: 20
H-index: 2
I10-index: 1
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