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

Xiaowen Lv,

Ali Basem, Mustafa Hasani

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 1, 2025

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

Language: Английский

Study on the thermal stability of a new siloxane working fluid modified by octamethylcyclotetrasiloxane and its application potential in organic Rankine cycle DOI
Wei Yu, Shukun Wang, Chao Liu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135017 - 135017

Published: Feb. 1, 2025

Language: Английский

Citations

0
The potential of combined robust model predictive control and deep learning in enhancing control performance and adaptability in energy systems DOI Creative Commons

Xiaowen Lv,

Ali Basem, Mustafa Hasani

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 1, 2025

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

Language: Английский

Citations

0