基于Stacking集成学习的无缝钢管连轧电耗预测  

Electricity consumption prediction for seamless steel pipe continuous rolling based on Stacking ensemble learning

作　　者：李一恒 孙抗[1] 赵来军[1] Li Yiheng;Sun Kang;Zhao Laijun(School of Electrical Engineering and Automationt,Henan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]河南理工大学电气工程与自动化学院,焦作454000

出　　处：《电子测量技术》2024年第8期53-60,共8页Electronic Measurement Technology

基　　金：河南省科技攻关计划项目(202102210092)资助。

摘　　要：无缝钢管生产作为高能耗行业的典型代表,其节能降耗一直都备受关注。通过预测电耗,企业可以找到节能降耗的有效途径,从而减少生产过程中的电能消耗,提升生产效率。为提高无缝钢管连轧电耗预测精度,采用一种改进的Stacking集成学习模型对电耗进行预测。首先,对采集到的电耗数据进行预处理,并基于嵌入法采用XGBoost和LightGBM进行特征选择;然后,采用随机搜索和贝叶斯优化结合的方法对基学习器开展超参数优化,在Stacking集成模型的首层中,选择LightGBM、ET和MLP作为基学习器;最后,根据基学习器在数据上的预测表现来赋予它们相应的权重,同时将原数据集也加入元学习器训练。结果表明:改进的Stacking集成学习模型具有最好的预测效果,其R^(2)为0.975 7,预测精度比单一基学习器和传统的Stacking集成学习模型都要高,证明了所提方法的有效性。Seamless steel pipe production,as a typical representative of high-energy-consuming industries,has always been a focus of energy-saving and consumption reduction.By predicting power consumption,enterprises can identify effective ways to save energy,thereby reducing electricity consumption in the production process and improving production efficiency.In order to improve the accuracy of electricity consumption prediction for seamless steel pipe continuous rolling,an improved Stacking ensemble learning model is adopted to predict power consumption.Firstly,the collected power consumption data is preprocessed,and XGBoost and LightGBM are used for feature selection based on embedding method.Then,a combination of random search and Bayesian optimization is used to optimize the hyperparameters of the base learners.In the first layer of the Stacking ensemble model,LightGBM,ET,and MLP are selected as the base learners.Finally,based on the predictive performance of the base learners on the data,they are assigned corresponding weights,and the original dataset is also included in the training of the meta-learner.The results show that the improved Stacking ensemble learning model has the best prediction effect,with an R~2 of 0.975 6.The prediction accuracy is higher than that of single base learners and traditional Stacking ensemble learning models,demonstrating the effectiveness of the proposed method.

关 键 词：无缝钢管 Stacking集成学习 电耗预测 

分 类 号：TN98[电子电信—信息与通信工程]