Seung Jun Lee

Long-horizon multivariate forecasting of nuclear power plant parameters becomes increasingly challenging as the forecast horizon and the number of target variables grow. This study evaluates an long short-term memory (LSTM) autoencoder-based latent trajectory forecasting framework for operator-support-oriented prediction using compact nuclear simulator data. Instead of directly regressing the full future trajectory in the observation space, the proposed framework compresses future trajectories into low-dimensional latent coordinates and predicts those coordinates from short observation histories. Three repeated-seed experiments were conducted to compare direct and latent forecasting across forecast horizons and latent dimensions. Within the direct LSTM baseline considered in this study, direct observation-space forecasting remained competitive at short horizons; whereas, latent trajectory forecasting became increasingly advantageous as the horizon increased. In the 1500-step, 25-channel task, the latent model reduced root mean square error (RMSE) by approximately 16% compared with the direct LSTM baseline while using substantially fewer parameters. Latent-dimension sweeps showed that the smallest bottleneck can have limited representational margin, whereas moderate-to-large dimensions form a broad practical operating range rather than a single universal optimum. These findings suggest that long-horizon nuclear plant parameter prediction can be treated as a conditional trajectory-generation problem in which a short recent plant history is mapped to a compact future-trajectory coordinate, providing a practical, parameter-efficient design direction for AI-assisted plant monitoring and operator support.