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

• Published in: Journal of organizational and end user computing Vol. 34; no. 2; pp. 1 - 17
• Main Authors: Salman, Rahman A. B. M, Myeongbae, Lee, Jonghyun, Lim, Cho, Yongyun, Changsun, Shin
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.03.2022
• Subjects: Big Data; Coefficient of variation; Company business management; Comparative studies; Data analysis; Datasets; Economic analysis; Economic development; Economic growth; Energy industries; Energy industry; Error analysis; Forecasts and trends; Industrial energy; Market trend/market analysis; Prediction models; Product management; Regression models; Root-mean-square errors; Statistical analysis; Statistical models; Support vector machines; South Korea
• ISSN: 1546-2234; 1546-5012
• DOI: 10.4018/JOEUC.291559

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.