ZHANG S F, LI Y X, QU C S, et al. Improved GRU-based adaptive extraction model for energy consumption characteristics in steel production [J]. Journal of Henan Polytechnic University(Natural Science) , 2025, 44(4): 1-10.

doi:10.16186/j.cnki.1673-9787.2024070025

Received:2024/07/05

Revised:2024/09/30

Published:2025/06/19

Improved GRU-based adaptive extraction model for energy consumption characteristics in steel production

Zhang Shufen1,2,3, Li Yuxin1,2, Qu Changsheng1,2, Gu Zheng1,2, Gao Rui1,2

1.School of Science， North China University of Science and Technology， Tangshan  063210， Hebei， China；2.Hebei Provincial Key Laboratory of Data Science and Application， Tangshan  063210， Hebei， China；3.Tangshan Key Laboratory of Data Science， Tangshan  063210， Hebei， China

Abstract: Objectives To address the insufficient energy consumption feature extraction in traditional models for different steel production processes，   Methods An Adaptive Feature Extraction Model （AGRU-Attention） was proposed. The model improved the GRU structure by introducing an adaptive mechanism that dynamically adjust the weights of input features. First， the Adaptive Gating Unit （AGU） was used to dynamically adjust the weights of the features， allowing the model to more accurately focus on the features crucial for energy consumption prediction. Secondly， the adjusted features were fully extracted through the GRU layer. Finally， the extracted features were weighted using the attention mechanism and the weighted features were input into the fully-connected layer for the predictive output. To verify the adaptive ability of the proposed model， two datasets with different sources and data volumes were compared with linear regression， support vector machines， CNN， LSTM and the sequence-to-sequence GRU model proposed in the literature ［19］.  Results The results showed that the prediction accuracy of the AGRU-Attention model was significantly better than the other models in the two different datasets. Compared with the GRU model， the AGRU-Attention model  achieves significant reductions of 99.99% in MSE， 99.71% in RMSE， and 99.67% in MAE on Dataset 1. On Dataset 2， the proposed model demonstrates reductions of 98.64% in MSE， 88.36% in RMSE， and 91.27% in MAE，respectively. It verified that the model had a higher practical application value in predicting production energy consumption.   Conclusions The proposed model not only realized adaptive adjustment of input feature weights to accurately extract features from different datasets， but also weighted the features through the attention mechanism，significantly improved the accuracy of the model’s predictions. 

Key words: adaptive feature extraction; attention mechanism; steel production; energy consumption data