Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters

Song, Houde; Liu, Xiaojing; Song, Meiqi

doi:10.1016/j.apenergy.2023.121077

Cited by 18 publications

(1 citation statement)

References 52 publications

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“…To enhance forecast accuracy, researchers initially employed traditional models such as autoregressive integral moving average (ARIMA) [ 5 ] and support vector regression (SVR) [ 6 , 7 ] to explore nuclear power forecasting. In the following years, artificial neural networks (ANNs) [ 8 ], convolutional neural networks (CNNs) [ 9 ], recurrent neural networks (RNNs), long short-term memory networks (LSTMs), gated recurrent units (GRUs) [ 10 ], etc., along with their variants, were employed to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants

Zhang

Xiao³

et al. 2023

Entropy

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Accurately predicting severe accident data in nuclear power plants is of utmost importance for ensuring their safety and reliability. However, existing methods often lack interpretability, thereby limiting their utility in decision making. In this paper, we present an interpretable framework, called GRUS, for forecasting severe accident data in nuclear power plants. Our approach combines the GRU model with SHAP analysis, enabling accurate predictions and offering valuable insights into the underlying mechanisms. To begin, we preprocess the data and extract temporal features. Subsequently, we employ the GRU model to generate preliminary predictions. To enhance the interpretability of our framework, we leverage SHAP analysis to assess the contributions of different features and develop a deeper understanding of their impact on the predictions. Finally, we retrain the GRU model using the selected dataset. Through extensive experimentation utilizing breach data from MSLB accidents and LOCAs, we demonstrate the superior performance of our GRUS framework compared to the mainstream GRU, LSTM, and ARIMAX models. Our framework effectively forecasts trends in core parameters during severe accidents, thereby bolstering decision-making capabilities and enabling more effective emergency response strategies in nuclear power plants.

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