  Prognosis of the reactor accident is a crucial way to ensure appropriate
strategies are adopted to avoid radioactive releases. However, there is very
limited research in the field of nuclear industry. In this paper, we propose a
method for accident prognosis based on the Temporal Fusion Transformer (TFT)
model with multi-headed self-attention and gating mechanisms. The method
utilizes multiple covariates to improve prediction accuracy on the one hand,
and quantile regression methods for uncertainty assessment on the other. The
method proposed in this paper is applied to the prognosis after loss of coolant
accidents (LOCAs) in HPR1000 reactor. Extensive experimental results show that
the method surpasses novel deep learning-based prediction methods in terms of
prediction accuracy and confidence. Furthermore, the interference experiments
with different signal-to-noise ratios and the ablation experiments for static
covariates further illustrate that the robustness comes from the ability to
extract the features of static and historical covariates. In summary, this work
for the first time applies the novel composite deep learning model TFT to the
prognosis of key parameters after a reactor accident, and makes a positive
contribution to the establishment of a more intelligent and staff-light
maintenance method for reactor systems.
