Deep Learning and Machine Learning Models to Predict Energy Consumption in Steel Industry
Kittisak Kerdprasop, Nittaya Kerdprasop, Paradee Chuaybamroong
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.