Deep learning-based dual monitoring system for power forecasting and fault detection in nuclear power applications

Monitoring key parameters in nuclear power plant control rooms is critical, as human errors can result in severe safety and operational consequences. This study proposes a hybrid framework for power prediction and fault detection that integrates multi-head self-attention mechanisms with long short-term memory networks, combined with a dual-monitoring system. The framework is evaluated using real-time data from two pressurized water reactor units (Units 5 and 6) under four realistic operational scenarios. In the most informative case, the model achieves a 56.6% reduction in root mean square error and a 36.8% reduction in mean absolute error, with a coefficient of determination ( R 2 ) of 0.9924—significantly outperforming the next-best benchmark. For fault diagnosis, the dual-monitoring system reduces the false negative rate to 18.73% and improves recall to 81.27%, demonstrating strong anomaly detection under complex conditions. By combining short-term fluctuation sensitivity with long-term trend stability, the proposed approach offers a robust and generalizable solution for intelligent monitoring. These findings advance the development of artificial intelligence–enhanced systems for secure and efficient operation of critical energy infrastructure. • Deep learning improves power forecasting in nuclear energy systems. • Dual-monitoring captures high- and low-frequency trends for fault detection. • Prediction error reduced by 36.8% across multiple reactor datasets. • Robust generalization shown in four real-world operational scenarios. • Supports real-time monitoring at 1-second intervals with low latency.