Multivariate Time Series Prediction for Loss of Coolant Accidents With a Zigmoid-Based LSTM
Shanshan Gong, Suyuan Yang, Jingke She, Weiqi Li, Shaofei Lu
Abstract
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.