13 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.…
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…
FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems
Maxime Schwarzer, Laurin Holz, Tobias Huerten +4
Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) deployed in energy infrastructure are vulnerable to model theft attacks, which allow adversaries to create evas…
AI Model Extraction Attacks: Bypassing Single-Client Assumptions in Defenses
Maxime Schwarzer, Johannes F. Loevenich, Gustavo Sánchez +5
Ensuring the protection of Artificial Intelligence (AI) models deployed in military Command and Control (C2) systems and critical infrastructure is essential for maintaining inform…
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…
Mechanism-Dependent Antagonism of Auxiliary Information in Substation-Level Load Disaggregation for Distribution Network Planning
Xuanhao Mu, Kundan Thota, Nan Liu +3
Open-source energy system models disaggregate zonal electricity demand to substations through Voronoi-based preprocessing pipelines that combine socioeconomic weighting with auxili…
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…
Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting
Max Kleinebrahm, Jonathan Berrisch, Philipp Eiser +11
Energy forecasting research faces a persistent comparability gap that makes it difficult to measure consistent progress over time. Reported accuracy gains are often not directly co…
CyberCertBench: Evaluating LLMs in Cybersecurity Certification Knowledge
Gustav Keppler, Ghada Elbez, Veit Hagenmeyer
The rapid evolution and use of Large Language Models (LLMs) in professional workflows require an evaluation of their domain-specific knowledge against industry standards. We introd…
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…
CritBench: A Framework for Evaluating Cybersecurity Capabilities of Large Language Models in IEC 61850 Digital Substation Environments
Gustav Keppler, Moritz Gstür, Veit Hagenmeyer
The advancement of Large Language Models (LLMs) has raised concerns regarding their dual-use potential in cybersecurity. Existing evaluation frameworks overwhelmingly focus on Info…
Stochastic Model Predictive Control based on Mixed Random Variables for Economic Energy Management
Janik Pinter, Maximilian Beichter, Ralf Mikut +2
Optimal scheduling of batteries has significant potential to reduce electricity costs and to enhance grid resilience. However, effective battery scheduling must account for both ph…