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IJML 2023 Vol.13(4): 142-145
DOI: 10.18178/ijml.2023.13.4.1142

Deep Learning and Machine Learning Models to Predict Energy Consumption in Steel Industry

Kittisak Kerdprasop*, Nittaya Kerdprasop, and Paradee Chuaybamroong

Manuscript received January 10, 2023; revised February 20, 2023; accepted March 28, 2023.

Abstract—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.

Index Terms—Energy consumption prediction, deep learning, machine learning, long short-term memory, ensemble model

Kittisak Kerdprasop and Nittaya Kerdprasop are with the School of Computer Engineering, Suranaree University of Technology, Thailand.
Paradee Chuaybamroong is with the Department of Environmental Science, Thammasat University, Thailand.
*Correspondence: KittisakThailand@gmail.com (K.K.)


Cite: Kittisak Kerdprasop*, Nittaya Kerdprasop, and Paradee Chuaybamroong, "Deep Learning and Machine Learning Models to Predict Energy Consumption in Steel Industry," International Journal of Machine Learning vol. 13, no. 4, pp. 142-145, 2023.

Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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