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cs.LG2026
Physics-Informed State Space Models for Day-Ahead Photovoltaic Power Forecasting
Phil Aupke, Muhammad Hassan, Khalid Ali +3
Accurate day-ahead photovoltaic (PV) power forecasting requires both strong temporal modeling and adherence to physical PV system constraints. Purely data-driven models often ignor…
cs.CR2026
Robustness of Local Energy Markets to Cyberattacks: Case Study of False Data Injection
Mehran Moradi, Reza Zamani, Phil Aupke +2
Market clearing in community-based local energy markets relies on power demand and PV forecasts, making it vulnerable to coordinated false data injection (FDI) attacks. This paper…
cs.LG2026
RNM-TD3: N:M Semi-structured Sparse Reinforcement Learning From Scratch
Isam Vrce, Andreas Kassler, Gökçe Aydos
Sparsity is a well-studied technique for compressing deep neural networks (DNNs) without compromising performance. In deep reinforcement learning (DRL), neural networks with up to…