Abstract
In the operation of nuclear power plants, the accurate prediction of power change trends is crucial for ensuring safety and stability. In this work, a ML-GRU-RS method, based on model-agnostic meta-learning (MAML), gate recurrent unit (GRU), and random search optimization, is proposed for the long-term prediction of key parameters of nuclear power plants. This method combines the fast adaptability of MAML, the time series data processing capability of GRU, and the optimization efficiency of random search to achieve high-precision predictions under varying power conditions. The results demonstrate that this method can effectively predict the future trends of key parameters in nuclear power plants. The ability of operators to anticipate these trends has been significantly enhanced, contributing to the overall safety of the nuclear power plants.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Nuclear Reactor Design and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
