5 citations · 5 across the 3 of their papers we have counts for
5 papers
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…
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…
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…
Smart Manufacturing: MLOps-Enabled Event-Driven Architecture for Enhanced Control in Steel Production
Bestoun S. Ahmed, Tommaso Azzalin, Andreas Kassler +2
We explore a Digital Twin-Based Approach for Smart Manufacturing to improve Sustainability, Efficiency, and Cost-Effectiveness for a steel production plant. Our system is based on…
Quantum Machine Learning in Climate Change and Sustainability: a Review
Amal Nammouchi, Andreas Kassler, Andreas Theorachis
Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quant…