Prediction of Food Factory Energy Consumption Using MLP and SVR Algorithms

Hyungah Lee, Dongju Kim and Jae-Hoi Gu ()
Additional contact information
Hyungah Lee: Energy Environment IT Convergence Group, Plant Engineering Center, Institute for Advanced Engineering, Yongin 17180, Republic of Korea
Dongju Kim: Energy Environment IT Convergence Group, Plant Engineering Center, Institute for Advanced Engineering, Yongin 17180, Republic of Korea
Jae-Hoi Gu: Energy Environment IT Convergence Group, Plant Engineering Center, Institute for Advanced Engineering, Yongin 17180, Republic of Korea

Energies, 2023, vol. 16, issue 3, 1-21

Abstract: The industrial sector accounts for a significant proportion of total energy consumption. Factory Energy Management Systems (FEMSs) can be a measure to reduce energy consumption in the industrial sector. Therefore, machine learning (ML)-based electricity and liquefied natural gas (LNG) consumption prediction models were developed using data from a food factory. By applying these models to FEMSs, energy consumption can be reduced in the industrial sector. In this study, the multilayer perceptron (MLP) algorithm was used for the artificial neural network (ANN), while linear, radial basis function networks and polynomial kernels were used for support vector regression (SVR). Variables were selected through correlation analysis with electricity and LNG consumption data. The coefficient of variation of root mean square error (CvRMSE) and coefficient of determination ( R 2 ) were examined to verify the prediction performance of the implemented models and validated using the criteria of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers Guideline 14. The MLP model exhibited the highest prediction accuracy for electricity consumption (CvRMSE: 17.35% and R 2 : 0.84) and LNG consumption (CvRMSE: 12.52% and R 2 : 0.88). Our findings demonstrate it is possible to attain accurate predictions of electricity and LNG consumption in food factories using relatively simple data.

Keywords: machine learning; artificial neural network; support vector regression; energy consumption prediction; food factory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/3/1550/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/3/1550/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:3:p:1550-:d:1057513

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().