Reference Hub9
A Comparative Study of Energy Big Data Analysis for Product Management in a Smart Factory

A Comparative Study of Energy Big Data Analysis for Product Management in a Smart Factory

Rahman A. B. M. Salman, Lee Myeongbae, Lim Jonghyun, Yongyun Cho, Shin Changsun
Copyright: © 2022 |Volume: 34 |Issue: 2 |Pages: 17
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781799893257|DOI: 10.4018/JOEUC.291559
Cite Article Cite Article

MLA

Salman, Rahman A. B. M., et al. "A Comparative Study of Energy Big Data Analysis for Product Management in a Smart Factory." JOEUC vol.34, no.2 2022: pp.1-17. http://doi.org/10.4018/JOEUC.291559

APA

Salman, R. A., Myeongbae, L., Jonghyun, L., Cho, Y., & Changsun, S. (2022). A Comparative Study of Energy Big Data Analysis for Product Management in a Smart Factory. Journal of Organizational and End User Computing (JOEUC), 34(2), 1-17. http://doi.org/10.4018/JOEUC.291559

Chicago

Salman, Rahman A. B. M., et al. "A Comparative Study of Energy Big Data Analysis for Product Management in a Smart Factory," Journal of Organizational and End User Computing (JOEUC) 34, no.2: 1-17. http://doi.org/10.4018/JOEUC.291559

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Energy has been obtained as one of the key inputs for a country's economic growth and social development. Analysis and modeling of industrial energy are currently a time-insertion process because more and more energy is consumed for economic growth in a smart factory. This study aims to present and analyse the predictive models of the data-driven system to be used by appliances and find out the most significant product item. With repeated cross-validation, three statistical models were trained and tested in a test set: 1) General Linear Regression Model (GLM), 2) Support Vector Machine (SVM), and 3) boosting Tree (BT). The performance of prediction models measured by R2 error, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Variation (CV). The best model from the study is the Support Vector Machine (SVM) that has been able to provide R2 of 0.86 for the training data set and 0.85 for the testing data set with a low coefficient of variation, and the most significant product of this smart factory is Skelp.