Lv Xiaowen, Basem Ali, Hasani Mohammadtaher, Sun Ping, Zhang Jingyu
College of Information Science and Technology, ZheJiang ShuRen University, Hangzhou, 310015, China.
Faculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, Iraq.
Sci Rep. 2025 Apr 1;15(1):11187. doi: 10.1038/s41598-025-95636-0.
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.
本研究探讨了鲁棒模型预测控制（RMPC）与深度学习的集成，以提高能源系统的性能和适应性，重点关注热电联产（CHP）、氢能发电以及甲烷合成气应用。所提出的框架将RMPC的鲁棒性与深度学习的学习和适应能力相结合，提高了控制精度和运行效率。大量仿真表明，与传统方法相比，集成的RMPC-深度学习系统将控制精度提高了8.02%，同时还将能源消耗降低了12.14%。这些定量结果证明了所提出的系统在应对诸如操作员饱和等挑战方面的有效性，展示了其在动态条件下优化能源系统的潜力。这项工作突出了将RMPC与深度学习相结合的变革性作用，为复杂应用中的能源管理提供了一种强大且适应性强的解决方案。
