@article{M3A8C3F5C,
title = "An Energy Consumption Prediction Model for Smart Factory Using Data Mining Algorithms",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2020",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2020.9.5.153",
author = "Sathishkumar V E/Myeongbae Lee/Jonghyun Lim/Yubin Kim/Changsun Shin/Jangwoo Park/Yongyun Cho",
keywords = "Energy Consumption, Data Mining, Random Forest, Linear Regression, Gradient Boosting Machine, Support Vector Machine",
abstract = "Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type.  Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set."
}