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 |Pages: 17
DOI: 10.4018/JOEUC.291559
Article PDF Download
Open access articles are freely available for 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.
Article Preview
Top

Introduction

Energy is the most significant and vital requirement for all living things on earth to survive and grow. Energy has been seen as one of the key inputs for a country's economic growth and social development. The rise of industrialization raises energy demand, which is a critical component of national strategy. Moreover, energy consumption rises in tandem with economic development and human progress. Nowadays, more and more energy is being used for economic growth and population growth. Facilities of industrial customers and the use of electricity to process various types of machinery, manufacture or assemble products, include such diverse industries as production, mining, and construction (Liye Xiao et al., 2016). Ultimately, more than one-third of electrical energy is used by those industrial sectors from total energy for a country. As a result, they begin to collect huge databases in order to gather valuable information. Authorities plan to use this data to improve the industry's standards and long-term viability. As a result, industrial authorities optimize the utilization of diverse energy resources to minimize energy consumption expenses.

The industrial sector is one of the primary sectors that need energy stability. Since the 1990s, South Korea's manufacturing industry has continued to expand rapidly and has become the main driving force of South Korean economies. Primary energy consumption rose at an annual rate of 7.5% in the 1990s, which in the same period was higher than the annual economic growth rate of 6.5%. This was due to the rapid growth of energy-intensive factories and petrochemical industries as well. The sharp increase in industrial electricity consumption helped increase energy conversion loss, further reducing the energy intensity (Kim et al., 2001). The increase in energy industry output after 2009 greatly buffered the nation against the global financial crisis, but it negatively affected the overall energy performance of the country. Several unpredictable factors influence the energy usage of industries, such as the nature of the market, the level of technology, energy costs, economic size, and national policy.

The achievements of the third scientific and technological revolution have simplified life. Industrial production is a significant sector for both the country and the nation, and it acts as a major financial indicator. In traditional sectors, it has boosted new technologies and systemic transformation. In traditional industries, the fruitful successes of the third scientific and technical revolution have encouraged people's lives and promoted technological progress and institutional change. The production sector is a vital industry and a primary predictor of a nation or region's economic level. Many advanced manufacturing countries already have advanced industries. Still, they continue to explore new opportunities and overhaul their manufacturing industries in order to ensure an unstoppable role in the face of modernization and technological growth. Germany is a common example, as the 'Industry 4.0' focuses on intelligent growth, emphasizing product quality, resource use, and energy use (Liye Xiao et al., 2016).

Many studies have shown that improving energy efficiency is very important for economic growth (David G.Ockwell., 2008, Chirs Bataille et al., 2017). The relationship between economic development, trade, and resources in Asia was examined by Nasreen and Anwar (Nasreen et al., 2014), and they found that economic growth and trade transparency had a positive effect on the use of energy. While several researchers have confirmed the one-way relationship between economics and energy (Lee, C.C., 2005, Tasni, S.Z., 2010), others have shown a two-way relationship (Cheng et al., 2004, Stern, D.I.A., 2000). Industrial factory owners are also beginning to realize that analysing and forecasting the energy data with the production data is very important for the benefit of their companies or plants. This issue is caused by unregulated energy use, such as overconsumption, weak systems, and waste energy. Energy is regarded as one of the most important and precious resources due to the continual growth in demand. It is imperative to engage with the management board of industrial companies as a supporting technical hand to enhance their energy usage. (A.B.M. Salman Rahman et al., 2019).

Complete Article List

Search this Journal:
Reset
Volume 36: 1 Issue (2024)
Volume 35: 3 Issues (2023)
Volume 34: 10 Issues (2022)
Volume 33: 6 Issues (2021)
Volume 32: 4 Issues (2020)
Volume 31: 4 Issues (2019)
Volume 30: 4 Issues (2018)
Volume 29: 4 Issues (2017)
Volume 28: 4 Issues (2016)
Volume 27: 4 Issues (2015)
Volume 26: 4 Issues (2014)
Volume 25: 4 Issues (2013)
Volume 24: 4 Issues (2012)
Volume 23: 4 Issues (2011)
Volume 22: 4 Issues (2010)
Volume 21: 4 Issues (2009)
Volume 20: 4 Issues (2008)
Volume 19: 4 Issues (2007)
Volume 18: 4 Issues (2006)
Volume 17: 4 Issues (2005)
Volume 16: 4 Issues (2004)
Volume 15: 4 Issues (2003)
Volume 14: 4 Issues (2002)
Volume 13: 4 Issues (2001)
Volume 12: 4 Issues (2000)
Volume 11: 4 Issues (1999)
Volume 10: 4 Issues (1998)
Volume 9: 4 Issues (1997)
Volume 8: 4 Issues (1996)
Volume 7: 4 Issues (1995)
Volume 6: 4 Issues (1994)
Volume 5: 4 Issues (1993)
Volume 4: 4 Issues (1992)
Volume 3: 4 Issues (1991)
Volume 2: 4 Issues (1990)
Volume 1: 3 Issues (1989)
View Complete Journal Contents Listing