基于深度学习和机器学习模型的钢铁行业能耗预测

Kittisak Kerdprasop, Nittaya Kerdprasop, Paradee Chuaybamroong
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摘要

本文介绍了利用基于机器学习和深度学习技术的建模方法预测钢铁行业能耗的研究结果。在这项工作中使用的机器学习算法包括人工神经网络(ANN)、k近邻(kNN)、随机森林(RF)和梯度增强(GB)。深度学习技术就是长短期记忆(LSTM)。本比较研究也采用基于统计的学习算法线性回归作为基准。建模结果表明,在基于统计和基于机器学习的技术中,GB和RF是预测能耗的最佳模型,而人工神经网络的预测性能与线性回归模型相当。然而,LSTM在预测工业能源消耗方面优于基于统计和基于机器学习的算法。
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Deep Learning and Machine Learning Models to Predict Energy Consumption in Steel Industry
This paper present the study results of predicting energy consumption in the steel industry using modeling methods based on machine learning and deep learning techniques. Machine learning algorithms used in this work include artificial neural network (ANN), k-nearest neighbors (kNN), random forest (RF), and gradient boosting (GB). Deep learning technique is long short-term memory (LSTM). Linear regression, which is the statistical-based learning algorithm, is also applied to be the baseline of this comparative study. The modeling results reveal that among the statistical-based and machine learning-based techniques, GB and RF are the best two models to predict energy consumption, whereas ANN shows the predictive performance comparable to the linear regression model. Nevertheless, LSTM outperforms both statistical-based and machine learning-based algorithms in predicting industrial energy consumption.
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