Temporal Re-Attention LSTM for Multivariate Prediction in Industrial Heating Process With Large Time Delays

The heating process contributes to over 60% of the energy consumption in material manufacturing, such as injection molding, metallurgy, die casting, etc. Temperature prediction is the crucial first step to realize energy management. However, the complex heating process poses challenges, such as strong multivariate coupling, large time delay, and frequent changes of heat transfer characteristics. To address these issues, this article introduces a temporal re-attention long short-term memory (TRA-LSTM) method for multivariate time series prediction. We adapt an encoder–decoder framework. A temporal attention module is utilized for extracting key historical features. A re-attention incorporated loss layer is proposed to rectify the temporal attention. TRA-LSTM's performance is compared with five advanced time series prediction methods across six predictions on four real-world datasets collected, including data from the injection molding barrel heating process and the battery thermal runaway process. Results show that TRA-LSTM consistently outperformed the other methods with high accuracy and efficiency. The alignment of re-attention weights with tested time delays in both heating and cooling processes further demonstrates TRA-LSTM's advantage. Ablation experiments underscore the significant contribution of the re-attention module, while hyperparameter sensitivity analysis offers guidance on selection and validates TRA-LSTM's robustness. Overall, the proposed method enables precise multivariate time series prediction and shows substantial potential for optimizing energy management in industrial production.