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

Author:

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

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.

Funder

Ministry of Science and Technology of the People’s Republic of China

Ministry of Industry and Information Technology of the People’s Republic of China

Publisher

Frontiers Media SA

Subject

Economics and Econometrics,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment

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