Towards the Prediction of Electricity Prices at the Intraday Market Using Shallow and Deep-Learning Methods. - Christoph Scholz 001; Malte Lehna; Katharina Brauns; André Baier (MIDAS@PKDD/ECML, 2020)

A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operator. - Malte Lehna; Björn Hoppmann; René Heinrich; Christoph Scholz 001 (CoRR, 2021)

Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents. - Malte Lehna; Jan Viebahn; Christoph Scholz 001; Antoine Marot; Sven Tomforde (CoRR, 2023)

HUGO - Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach. - Malte Lehna; Clara Holzhüter; Sven Tomforde; Christoph Scholz 001 (CoRR, 2024)

Fault Detection for agents on power grid topology optimization: A Comprehensive analysis. - Malte Lehna; Mohamed Hassouna; Dmitry Degtyar; Sven Tomforde; Christoph Scholz 001 (CoRR, 2024)

Targeted adversarial attacks on wind power forecasts. - René Heinrich; Christoph Scholz 001; Stephan Vogt; Malte Lehna (Mach. Learn., 2024)

Structured Power Grid Simulation Dataset for Machine Learning: Failure and Survival Events in Grid2Op's L2RPN WCCI 2022 Environment. - Malte Lehna; Mohamed Hassouna; Dmitry Degtyar; Sven Tomforde; Christoph Scholz 001 (Zenodo, 2024)

Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach. - Mohamed Hassouna; Clara Holzhüter; Malte Lehna; Matthijs de Jong; Jan Viebahn; Bernhard Sick; Christoph Scholz 001 (CoRR, 2025)