NPP accident prevention: Integrated neural network for coupled multivariate time series prediction based on PSO and its application under uncertainty analysis for NPP data

Abstract

Due to the requirement of a cleaner, more sustainable form of energy production and the rapid development of DT, nuclear energy needs to be digital transformed. This paper aims at the time series prediction module of DT in NPP. Despite the widespread application of time series forecasting in various domains, the inherent uncertainties within the data and the selection of neural network hyperparameters pose a formidable challenge to accurate predictions. This study proposes a coupled multivariate prediction method and conducts a comparative analysis of three popular time series forecasting models: LSTM, CNN-LSTM, and Transformer. PSO hyperparameter optimization is also employed, accompanied by multiple error calculation metrics for model evaluation. It is noteworthy that the experimental data employed in this study are derived from the real operation process of a Gen II + reactor in the Chinese mainland, which is in the off-site power loss accident condition. Simultaneously, adopting a smaller training dataset proportion contributes to striking a balance among resource utilization, model performance, and experimental efficiency in the research. Experimental results unequivocally demonstrate that among these models, Transformer excels in multi-input multi-output time series forecasting. Moreover, uncertainty analysis using Bayesian generates a forecast band, proving the robustness of Transformer.