4 citations · 6 across the 9 of their papers we have counts for
5 papers · 1 filter
A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods
Matthias Hertel, Sebastian Pütz, Jonathan Kolar +3
Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts o…
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…
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…
Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators
Manuel Treutlein, Marc Schmidt, Roman Hahn +4
Distribution system operators (DSOs) must cope with new challenges such as the reconstruction of distribution grids along climate neutrality pathways or the ability to manage and c…
Transformer Training Strategies for Forecasting Multiple Load Time Series
Matthias Hertel, Maximilian Beichter, Benedikt Heidrich +4
In the smart grid of the future, accurate load forecasts on the level of individual clients can help to balance supply and demand locally and to prevent grid outages. While the num…