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

Rahman Salman, Lee Myeongbae, Lim Jonghyun, Yongyun Cho, Shin Changsun

OAI: oai:igi-global.com:291559 • 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.

End-User Computing Social Sciences and Humanities Human Aspects of Technology