Machine Learning and Multilayer Perceptron-Based Customized Predictive Models for Individual Processes in Food Factories

Byunghyun Lim, Dongju Kim, Woojin Cho and Jae-Hoi Gu ()
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Byunghyun Lim: Energy Environment IT Convergence Group, Energy Environment Research Center, Institute for Advanced Engineering, Yongin-si 17180, Republic of Korea
Dongju Kim: Energy Environment IT Convergence Group, Energy Environment Research Center, Institute for Advanced Engineering, Yongin-si 17180, Republic of Korea
Woojin Cho: Energy Environment IT Convergence Group, Energy Environment Research Center, Institute for Advanced Engineering, Yongin-si 17180, Republic of Korea
Jae-Hoi Gu: Energy Environment IT Convergence Group, Energy Environment Research Center, Institute for Advanced Engineering, Yongin-si 17180, Republic of Korea

Energies, 2025, vol. 18, issue 11, 1-22

Abstract: A factory energy management system, based on information and communication technology, facilitates efficient energy management using the real-time monitoring, analyzing, and controlling of the energy consumption of a factory. However, traditional food processing plants use basic control systems that cannot analyze energy consumption for each phase of processing. This makes it difficult to identify usage patterns for individual operations. This study identifies steam energy consumption patterns across four stages of food processing. Additionally, it proposes a customized predictive model employing four machine learning algorithms—linear regression, decision tree, random forest, and k-nearest neighbor—as well as two deep learning algorithms: long short-term memory and multi-layer perceptron. The enhanced multi-layer perceptron model achieved a high performance, with a coefficient of determination (R 2 ) of 0.9418, a coefficient of variation of root mean square error (CVRMSE) of 9.49%, and a relative accuracy of 93.28%. The results of this study demonstrate that straightforward data and models can accurately predict steam energy consumption for individual processes. These findings suggest that a customized predictive model, tailored to the energy consumption characteristics of each process, can offer precise energy operation guidance for food manufacturers, thereby improving energy efficiency and reducing consumption.

Keywords: energy consumption prediction; food factories; factory energy management system (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: 2025
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