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Prediction of Food Factory Energy Consumption Using MLP and SVR Algorithms

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hyungah Lee & Dongju Kim & Jae-Hoi Gu, 2023. "Prediction of Food Factory Energy Consumption Using MLP and SVR Algorithms," Energies, MDPI, vol. 16(3), pages 1-21, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1550-:d:1057513
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    References listed on IDEAS

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    1. Azadeh Sadeghi & Roohollah Younes Sinaki & William A. Young & Gary R. Weckman, 2020. "An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks," Energies, MDPI, vol. 13(3), pages 1-23, January.
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    Cited by:

    1. Hyungah Lee & Woojin Cho & Jong-hyeok Park & Jae-hoi Gu, 2024. "Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning," Energies, MDPI, vol. 17(10), pages 1-16, May.
    2. Woojin Cho & Hyungah Lee & Jae-hoi Gu, 2024. "Optimization Techniques and Evaluation for Building an Integrated Lightweight Platform for AI and Data Collection Systems on Low-Power Edge Devices," Energies, MDPI, vol. 17(7), pages 1-14, April.
    3. Agnieszka Wawrzyniak & Andrzej Przybylak & Piotr Boniecki & Agnieszka Sujak & Maciej Zaborowicz, 2023. "Neural Modelling in the Study of the Relationship between Herd Structure, Amount of Manure and Slurry Produced, and Location of Herds in Poland," Agriculture, MDPI, vol. 13(7), pages 1-13, July.
    4. Rubens A. Fernandes & Raimundo C. S. Gomes & Carlos T. Costa & Celso Carvalho & Neilson L. Vilaça & Lennon B. F. Nascimento & Fabricio R. Seppe & Israel G. Torné & Heitor L. N. da Silva, 2023. "A Demand Forecasting Strategy Based on a Retrofit Architecture for Remote Monitoring of Legacy Building Circuits," Sustainability, MDPI, vol. 15(14), pages 1-37, July.
    5. Oh, Jiyoung & Min, Daiki, 2024. "Prediction of energy consumption for manufacturing small and medium-sized enterprises (SMEs) considering industry characteristics," Energy, Elsevier, vol. 300(C).

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