Predicting nuclear power plant operational parameters using clustering and mutual information for feature selection and Transformer neural network optimized by TPE

Introduction In the domain of nuclear power plant operations, accurately and rapidly predicting future states is crucial for ensuring safety and efficiency. Data-driven methods are becoming increasingly important for nuclear power plant parameter forecasting. While Transformer neural networks have emerged as powerful tools due to their self-attention mechanisms and ability to capture long-range dependencies, their application in the nuclear energy field remains limited and their capabilities largely untested. Additionally, Transformer models are highly sensitive to data complexity, presenting challenges for model development and computational efficiency. Methods This study proposes a feature selection method that integrates clustering and mutual information techniques to reduce the dimensionality of training data before applying Transformer models. By identifying key physical quantities from large datasets, we refine the data used for training a Transformer model, which is then optimized using the Tree-structured Parzen Estimator algorithm. Results Applying this method to a dataset for predicting a shutdown condition of a nuclear power plant, we demonstrate the effectiveness of the proposed “feature selection + Transformer” approach: (1) The Transformer model achieved high accuracy in predicting nuclear power plant parameters, with key physical quantities such as temperature, pressure, and water level attaining a normalized root mean squared error below 0.009, indicating that the RMSE is below 0.9% of the range of the original data, reflecting a very small prediction error. (2) The feature selection method effectively reduced input data dimensionality with minimal impact on model accuracy. Discussion The results demonstrate that the proposed clustering and mutual information-based method provides an effective feature selection strategy that encapsulates operational information of the plant.