A STUDY OF THE MODELING ON THE SMART FACTORY PRODUCTION OPTIMIZATION USING ENERGY CONSUMPTION PREDICTION

Published 31 Jan 2022 •  vol 150  • 


Authors:

 

A.B.M.Salman Rahman, Department of Information & Communication Engineering, Sunchon National University, South Korea
Myeongbae Lee, Department of Information & Communication Engineering, Sunchon National University, South Korea
Jonghyun Lim, Department of Information & Communication Engineering, Sunchon National University, South Korea
Yongyun Cho, Department of Information & Communication Engineering, Sunchon National University, South Korea
Changsun Shin, Department of Information & Communication Engineering, Sunchon National University, South Korea

Abstract:

 

Energy consumption is a global issue that significantly impacts the nation's energy security. In the industrial sector, smart factory energy consumption analysis and forecasts are crucial for improving energy usage rates and creating profits. An industrial enterprise's energy analysis and forecasting are becoming progressively essential. As a result, this is an excellent time to lend a helping hand technologically to minimize energy consumption and deliver profitable outcomes for smart factories. It is really challenging to evaluate energy usage and make reliable estimations of industrial energy consumption. In a nutshell, this paper examines monthly electricity consumption to identify the discrepancy between energy utilization and energy demands. It represents the connection between energy consumption, energy demand, and various manufacturing goods on a graph. This study compares real data with forecasted data curves using the Autoregressive integrated moving average (ARIMA), and Seasonal Autoregressive integrated moving average(SARIMA) to improve energy usage. ARIMA and SARIMA have the Root Mean Square Error (RMSE) performance evaluations of 8.90 and 10.90, respectively. Finally, determine the most effective product for the smart factory to enhance the energy utilization rate and generate profit for the smart factory.

Keywords:

 

Smart Factory, Energy Consumption, Correlation, Moving Average,Data Forecasting, ARIMA, Seasonal ARIMA, Variable Importance

References:

 

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Citations:

 

APA:
Rahman, A. B. M. S., Lee, M., Lim, J., Cho, Y., & Shin, C., (2022). A Study of the Modeling on the Smart Factory Production Optimization Using Energy Consumption Prediction. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 150, 31-40. doi: 10.33832/ijast.2022.150.04.

MLA:
Rahman, A. B. M. Salman, et al. “A Study of the Modeling on the Smart Factory Production Optimization Using Energy Consumption Prediction.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 150, 2022, pp. 31-40. IJAST, http://article.nadiapub.com/IJAST/Vol150/4.html.

IEEE:
[1] A. B. M. S. Rahman, M. Lee, J. Lim, Y. Cho, and C. Shin, "A Study of the Modeling on the Smart Factory Production Optimization Using Energy Consumption Prediction." International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 150, pp. 31-40, January 2022.