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Artificial Neural Network Modelling and Experimental Evaluation of Dust and Thermal Energy Impact on Monocrystalline and Polycrystalline Photovoltaic Modules

Author

Listed:
  • Jabar H. Yousif

    (Faculty of Computing and IT, Sohar University, P.O. Box 44, Sohar PCI 311, Oman)

  • Hussein A. Kazem

    (Faculty of Engineering, Sohar University, P.O. Box 44, Sohar PCI 311, Oman)

  • Haitham Al-Balushi

    (Faculty of Computing and IT, Sohar University, P.O. Box 44, Sohar PCI 311, Oman)

  • Khaled Abuhmaidan

    (Faculty of Computing and IT, Sohar University, P.O. Box 44, Sohar PCI 311, Oman)

  • Reem Al-Badi

    (Faculty of Engineering, Sohar University, P.O. Box 44, Sohar PCI 311, Oman)

Abstract

Many environmental parameters affect the performance of solar photovoltaics (PV), such as dust and temperature. In this paper, three PV technologies have been investigated and experimentally analyzed (mono, poly, and flexible monocrystalline) in terms of the impact of dust and thermal energy on PV behavior. Furthermore, a modular neural network is designed to test the effects of dust and temperature on the PV power production of six PV modules installed at Sohar city, Oman. These experiments employed three pairs of PV modules (one cleaned daily and one kept dusty for 30 days). The performance of the PV power production was evaluated and examined for the three PV modules (monocrystalline, polycrystalline, and flexible), which achieved 30.24%, 28.94%, and 36.21%, respectively. Moreover, the dust reduces the solar irradiance approaching the PV module and reduces the temperature, on the other hand. The neural network and practical models’ performance were compared using different indicators, including MSE, NMSE, MAE, Min Abs Error, and r. The Mean Absolute Error (MAE) is used for evaluating the accuracy of the ANN machine learning model. The results show that the accuracy of the predicting power of the six PV modules was considerable, at 97.5%, 97.4%, 97.6%, 96.7%, 96.5%, and 95.5%, respectively. The dust negatively reduces the PV modules’ power production performance by about 1% in PV modules four and six. Furthermore, the results were evident that the negative effect of the dust on the PV module production based on the values of RMSE, which measures the square root of the average of the square’s errors. The average errors in predicting the power production of the six PV modules are 0.36406, 0.38912, 0.34964, 0.49769, 0.46486, and 0.68238.

Suggested Citation

  • Jabar H. Yousif & Hussein A. Kazem & Haitham Al-Balushi & Khaled Abuhmaidan & Reem Al-Badi, 2022. "Artificial Neural Network Modelling and Experimental Evaluation of Dust and Thermal Energy Impact on Monocrystalline and Polycrystalline Photovoltaic Modules," Energies, MDPI, vol. 15(11), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4138-:d:831754
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    2. Mohammad Firoozzadeh & Marzieh Lotfi & Amir Hossein Shiravi, 2022. "An Experimental Study on Simultaneous Use of Metal Fins and Mirror to Improve the Performance of Photovoltaic Panels," Sustainability, MDPI, vol. 14(24), pages 1-14, December.
    3. Elias Roumpakias & Tassos Stamatelos, 2023. "Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    4. Md Saif Hassan Onim & Zubayar Mahatab Md Sakif & Adil Ahnaf & Ahsan Kabir & Abul Kalam Azad & Amanullah Maung Than Oo & Rafina Afreen & Sumaita Tanjim Hridy & Mahtab Hossain & Taskeed Jabid & Md Sawka, 2022. "SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels," Energies, MDPI, vol. 16(1), pages 1-19, December.

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