河南理工大学学报（自然科学版）2025，Vol.44Issue(4)：1-10,10.DOI:10.16186/j.cnki.1673-9787.2024070025
改进GRU的钢铁生产能耗特征自适应提取模型
Improved GRU-based adaptive extraction model for energy consumption characteristics in steel production
摘要
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 dynami-cally 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 bet-ter 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 adap-tive 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.关键词
自适应特征提取/注意力机制/钢铁生产/能耗数据Key words
adaptive feature extraction/attention mechanism/steel production/energy consumption data分类
计算机与自动化引用本文复制引用
张淑芬,李雨欣,屈昌盛,谷铮,高瑞..改进GRU的钢铁生产能耗特征自适应提取模型[J].河南理工大学学报（自然科学版）,2025,44(4):1-10,10.基金项目
国家自然科学基金资助项目(U20A20179) （U20A20179）
