The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems
Abstract 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 comb...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-95636-0 |
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| author | Xiaowen Lv Ali Basem Mohammadtaher Hasani Ping Sun Jingyu Zhang |
| author_facet | Xiaowen Lv Ali Basem Mohammadtaher Hasani Ping Sun Jingyu Zhang |
| author_sort | Xiaowen Lv |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-a7fdf3459d6b44dca3b7465d6d06cb90 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a7fdf3459d6b44dca3b7465d6d06cb902025-08-20T01:56:01ZengNature PortfolioScientific Reports2045-23222025-04-0115112310.1038/s41598-025-95636-0The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systemsXiaowen Lv0Ali Basem1Mohammadtaher Hasani2Ping Sun3Jingyu Zhang4College of Information Science and Technology, ZheJiang ShuRen UniversityFaculty of Engineering, Warith Al-Anbiyaa UniversityDepartment of Computer Engineering, Science and Research Branch, Islamic Azad UniversityCollege of Information Science and Technology, ZheJiang ShuRen UniversitySchool of Computer & Communication Engineering, Changsha University of Science & TechnologyAbstract 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.https://doi.org/10.1038/s41598-025-95636-0RMPCDeep learningCHPPower-to-HydrogenPower-to-Gas methaneEnergy systems optimization |
| spellingShingle | Xiaowen Lv Ali Basem Mohammadtaher Hasani Ping Sun Jingyu Zhang The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems Scientific Reports RMPC Deep learning CHP Power-to-Hydrogen Power-to-Gas methane Energy systems optimization |
| title | The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems |
| title_full | The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems |
| title_fullStr | The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems |
| title_full_unstemmed | The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems |
| title_short | The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems |
| title_sort | potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems |
| topic | RMPC Deep learning CHP Power-to-Hydrogen Power-to-Gas methane Energy systems optimization |
| url | https://doi.org/10.1038/s41598-025-95636-0 |
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