50 citations · 137 across the 28 of their papers we have counts for
12 papers · 1 filter
Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Study
Hadeer Elashhab, Sai Srijan Papineni, Marvin Dorn +2
While publicly available electricity market data presents a valuable resource for forecasting research, the field lacks established benchmark datasets for standardized comparison.…
Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control
Lukas Zetto, Benjamin Schäfer, Qiong Huang
As the integration of volatile renewable energy sources increases the strain on modern power grids, the use of Reinforcement Learning (RL) for autonomous topological reconfiguratio…
Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting
Jan Niklas Lettner, Hadeer El Ashhab, Benjamin Schäfer
As renewable energy integration increases market volatility, probabilistic electricity price forecasting has become essential for effective risk management. However, current-proper…
Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
Matthias Hertel, Alexandra Nikoltchovska, Sebastian Pütz +3
Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, appli…
Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting
Jan Niklas Lettner, Hadeer El Ashhab, Veit Hagenmeyer +1
Large-scale renewable energy deployment introduces pronounced volatility into the electricity system, turning grid operation into a complex stochastic optimization problem. Accurat…
Explainable time-series forecasting with sampling-free SHAP for Transformers
Matthias Hertel, Sebastian Pütz, Ralf Mikut +2
Time-series forecasts are essential for planning and decision-making in many domains. Explainability is key to building user trust and meeting transparency requirements. Shapley Ad…