9 papers
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…
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…
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 Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
Hallah Shahid Butt, Qiong Huang, Gökhan Demirel +6
The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces s…
BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control
Felix Koch, Thomas Krug, Fabian Raisch +2
Machine learning (ML) is increasingly used for data-driven modeling of buildings to enable downstream tasks such as fault detection and diagnosis, and energy-efficient control. Whi…