DataSheet1_Multivariate-coupling LOCA prediction using zLSTM.ZIP

A novel deep learning model zLSTM, which evolves from Long-Short Term Memory (LSTM) with enhanced long-term processing capability, is applied to the prediction of Loss of Coolant Accident (LOCA). During the prediction process, six-dimensional multivariate coupling is established among six major system parameters after connecting each timestep with the time dimension. The demonstration experiments show that the proposed method can increase the prediction accuracy by 35.84% comparing to the traditional LSTM baseline. Furthermore, zLSTM model follows the parameter progress well at the starting stage of LOCA, which reduces the prediction error at both the beginning and the far end.

一种源自长短期记忆网络（Long-Short Term Memory, LSTM）、具备增强型长期处理能力的新型深度学习模型zLSTM，被应用于冷却剂流失事故（Loss of Coolant Accident, LOCA）的预测任务中。在预测过程中，该模型将每个时间步与时间维度关联后，在六大核心系统参数之间构建起六维多元耦合关系。验证实验结果表明，相较于传统LSTM基准模型，所提方法的预测精度提升了35.84%。此外，zLSTM模型能够很好地追踪冷却剂流失事故初期的参数变化趋势，有效降低了事故初始阶段与后期阶段的预测误差。