301 citations · 1.2k across the 57 of their papers we have counts for
10 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…
High-fidelity inference of power grid frequency distributions
Alessandro Lonardi, Benjamin Schäfer, Christian Beck
Precision measurements of power grid frequency track energy supply-demand imbalances, with persistent fluctuations indicating strain that can lead to outages. Characterizing the st…
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…
PowerSINDy: Identifying Nonlinear Time-Dependent Dynamics in Power Grid Frequency
Xinyi Wen, Xiao Li, Leonardo Rydin Gorjão +2
System identification plays a crucial role in physics and machine learning for discovering governing equations directly from data. A powerful approach is the Sparse Identification…
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…
Predicting power grid frequency dynamics with invertible Koopman-based architectures
Eric Lupascu, Xiao Li, Benjamin Schäfer
The system frequency is a critical measure of power system stability and understanding, and modeling it are key to ensure reliable power system operations. Koopman-based autoencode…