9 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…
Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics
Benedikt Kaas, Manuel Treutlein, Hannes Benedikt Gerber +5
Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation. However, current for…
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…
Knowledge Distillation for Efficient Transformer-Based Reinforcement Learning in Hardware-Constrained Energy Management Systems
Pascal Henrich, Jonas Sievers, Maximilian Beichter +3
Transformer-based reinforcement learning has emerged as a strong candidate for sequential control in residential energy management. In particular, the Decision Transformer can lear…
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…
Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimization
Maximilian Beichter, Nils Friederich, Janik Pinter +7
Time series foundation models provide a universal solution for generating forecasts to support optimization problems in energy systems. Those foundation models are typically traine…