Mixed-interval steam consumption modeling for industrial energy optimization via meta-learning through shared attention

Effective steam management supports cost control and carbon abatement in industrial processes. However, steam monitoring in industrial records often exhibits mixed sampling intervals. The mismatch in time interval creates a limited-data problem that conventional energy models often struggle to handle. Therefore, a model-agnostic meta-learning framework integrated with an attention-based long short-term memory network is proposed for steam-consumption prediction under limited-data conditions. Meta-training on related high-frequency source units learns shared attention parameters and enables rapid adaptation to a low-frequency target unit without requiring synthetic data generation. The performance of steam consumption prediction is validated using a large-scale case study of the crude glycerin purification process. The results demonstrate that the attention-based long short-term memory model outperforms traditional models with the highest coefficient of determination value (R2) of 0.772. The incorporation of meta-learning further enhances the prediction performance of the model, with a decrease in the prediction error from 168.891 to 123.777 kg/h and an improvement in R2 of 0.847. Furthermore, the energy-saving analysis indicates the reduction in annual steam consumption and greenhouse gas emissions of 4372.304 (11.63% reduction) and 613.815 tons, respectively.