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Developing Machine Learning-Based Intelligent Control System for Performance Optimization of Solar PV-Powered Refrigerators

Author

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  • Mohamed A. Eltawil

    (Department of Agricultural Systems Engineering, College of Agricultural and Food Sciences, King Faisal University, Al-Ahsa 31982, Saudi Arabia
    Agricultural Engineering Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El Sheikh 33516, Egypt)

  • Maged Mohammed

    (Date Palm Research Center of Excellence, King Faisal University, Al-Ahsa 31982, Saudi Arabia
    Agricultural and Biosystems Engineering Department, Faculty of Agriculture, Menoufia University, Shebin El Koum 32514, Egypt)

  • Nayef M. Alqahtani

    (Department of Agricultural Systems Engineering, College of Agricultural and Food Sciences, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

Abstract

Display refrigerators consume significantly high energy, and improving their efficiency is essential to minimize energy consumption and greenhouse gas emissions. Therefore, providing the refrigeration system with a reliable and energy-efficient mechanism is a real challenge. This study aims to design and evaluate an intelligent control system (ICS) using artificial neural networks (ANN) for the performance optimization of solar-powered display refrigerators (SPDRs). The SPDR was operated using the traditional control system at a fixed frequency of 60 Hz and then operated based on variable frequencies ranging from 40 to 60 Hz using the designed ANN-based ICS combined with a variable speed drive. A stand-alone PV system provided the refrigerator with the required energy at the two control options. For the performance evaluation, the operating conditions of the SPDR after the modification of its control system were compared with its performance with a traditional control system (TCS) at target refrigeration temperatures of 1, 3, and 5 °C and ambient temperatures of 23, 29, and 35 °C. Based on the controlled variable frequency speed by the modified control system (MCS), the power, energy consumption, and coefficient of performance (COP) of the SPDR are improved. The results show that both refrigeration control mechanisms maintain the same cooling temperature, but the traditional refrigerator significantly consumes more energy ( p < 0.05). At the same target cooling temperature, increasing the ambient temperature decreased the COP for the SPDR with both the TCS and MCS. The average daily COP of the SPDR varied from 2.8 to 3.83 and from 1.91 to 2.82 for the SPDR with the TCS and MCS, respectively. The comparison results of the two refrigerators’ conditions indicated that the developed ICS for the SPDR saved about 35.5% of the energy at the 5 °C target cooling temperature and worked with smoother power when the ambient temperature was high. The COP of the SPDR with the MCS was higher than the TCS by 26.37%, 26.59%, and 24.22% at the average daily ambient temperature of 23 °C, 29 °C, and 35 °C, respectively. The developed ANN-based control system optimized the SPDR and proved to be a suitable tool for the refrigeration industry.

Suggested Citation

  • Mohamed A. Eltawil & Maged Mohammed & Nayef M. Alqahtani, 2023. "Developing Machine Learning-Based Intelligent Control System for Performance Optimization of Solar PV-Powered Refrigerators," Sustainability, MDPI, vol. 15(8), pages 1-35, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6911-:d:1127776
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    References listed on IDEAS

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    1. Matthew Rowe & Timur Yunusov & Stephen Haben & William Holderbaum & Ben Potter, 2014. "The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction," Energies, MDPI, vol. 7(6), pages 1-24, May.
    2. Yuhe Gao & Jie Ji & Zewei Guo & Peng Su, 2018. "Comparison of the solar PV cooling system and other cooling systems," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 13(4), pages 353-363.
    3. Noro, M. & Lazzarin, R.M., 2014. "Solar cooling between thermal and photovoltaic: An energy and economic comparative study in the Mediterranean conditions," Energy, Elsevier, vol. 73(C), pages 453-464.
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