3 papers
cs.LG2026
A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN
Adrian Degenkolb, Qiong Huang, Benjamin Schäfer
Graph construction is a critical but underexamined design choice in deep reinforcement learning for power grid control. We present a controlled experimental comparison of different…
cs.LG2026
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…
cs.AI2026
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…