The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems

This study investigates the integration of Robust Model Predictive Control (RMPC) and Deep Learning to enhance the performance and adaptability of energy systems, focusing on Combined Heat and Power (CHP), Power-to-Hydrogen, and Power-to-Gas Methane applications. The proposed framework combines RMPC's robustness with Deep Learning's ability to learn and adapt, improving control precision and operational efficiency. Extensive simulations indicate that the integrated RMPC-Deep Learning system improves control accuracy by 8.02% compared to conventional methods, while also reducing energy consumption by 12.14%. These quantitative results demonstrate the effectiveness of the proposed system in addressing challenges such as operator saturation, showcasing its potential to optimize energy systems under dynamic conditions. This work highlights the transformative role of merging RMPC with Deep Learning, providing a robust and adaptable solution for energy management in complex applications.