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).