AUTHOR=Gong Shanshan , Yang Suyuan , She Jingke , Li Weiqi , Lu Shaofei 

TITLE=Multivariate Time Series Prediction for Loss of Coolant Accidents With a Zigmoid-Based LSTM

JOURNAL=Frontiers in Energy Research

VOLUME=10

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.852349

DOI=10.3389/fenrg.2022.852349

ISSN=2296-598X

ABSTRACT=<p>Post-LOCA prediction is of safety significance to NPP, but requires a processing coverage of non-linearity, both short and long-term memory, and multiple system parameters. To enable an ability promotion of previous LOCA prediction models, a new gate function called zigmoid is introduced and embedded to the traditional long short-term memory (LSTM) model. The newly constructed zigmoid-based LSTM (zLSTM) amplifies the gradient at the far end of the time series, which enhances the long-term memory without weakening the short-term one. Multiple system parameters are integrated into a 12-dimension input vector to the zLSTM for a comprehensive consideration based on which the LOCA prediction can be accurately generated. Experimental results show both accuracy evaluations and LOCA progression produced by the proposed zLSTM, and two baseline methods demonstrating the superiority of applying zLSTM to LCOA predictions.</p>