Modeling the Smart Factory Manufacturing Products Characteristics: The Perspective of Energy Consumption

• Published in: Discrete dynamics in nature and society Vol. 2021; pp. 1 - 15
• Main Authors: Salman Rahman, A.B.M., Lee, Myeongbae, Lim, Jonghyun, Cho, Yongyun, Shin, Changsun
• Format: Journal Article
• Language: English
• Published: New York Hindawi 31.12.2021; Hindawi Limited; Wiley
• Subjects: Autoregressive models; China; Data mining; Economic growth; Energy conservation; Energy consumption; Energy industry; Energy utilization; Factories; Forecasting; Industrial energy; Industrial plants; Internet of Things; Manufacturing; Modelling; Optimization; Pattern recognition; Performance evaluation; Root-mean-square errors; South Korea; South Korea; China
• ISSN: 1026-0226; 1607-887X
• DOI: 10.1155/2021/4415105

Economic progress is built on the foundation of energy. In the industrial sector, smart factory energy consumption analysis and forecasts are crucial for improving energy consumption rates and also for creating profits. The importance of energy analysis and forecasting in an industrial environment is increasing speedily. It is a great chance to provide a technical boost to smart factories looking to reduce energy usage and produce more profit through the control and optimization modeling. It is tough to analyze energy usage and make accurate estimations of industrial energy consumption. Consequently, this study examines monthly energy consumption to identify the discrepancy between energy usages and energy needs. It depicts the link between energy consumption, demand, and various industrial goods by pattern recognition. The correlation technique is utilized in this study to figure out the link between energy usage and the weight of various materials used in product manufacturing. Next, we use the moving average approach to calculate the monthly and weekly moving averages of energy usages. The use of data-mining techniques to estimate energy consumption rates based on production is increasingly prevalent. This study uses the autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) to compare the actual data with forecasting data curves to enhance energy utilization. The Root Mean Square Error (RMSE) performance evaluation result for ARIMA and SARIMA is 8.70 and 10.90, respectively. Eventually, the Variable Important technique determines the smart factory’s most essential product to enhance the energy utilization rate and obtain profitable items for the smart factory.