IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i11p4138-d831754.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/11/4138/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/11/4138/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hachicha, Ahmed Amine & Al-Sawafta, Israa & Said, Zafar, 2019. "Impact of dust on the performance of solar photovoltaic (PV) systems under United Arab Emirates weather conditions," Renewable Energy, Elsevier, vol. 141(C), pages 287-297.
    2. Felipe Leite Coelho da Silva & Kleyton da Costa & Paulo Canas Rodrigues & Rodrigo Salas & Javier Linkolk López-Gonzales, 2022. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector," Energies, MDPI, vol. 15(2), pages 1-12, January.
    3. Klugmann-Radziemska, Ewa, 2015. "Degradation of electrical performance of a crystalline photovoltaic module due to dust deposition in northern Poland," Renewable Energy, Elsevier, vol. 78(C), pages 418-426.
    4. Saidan, Motasem & Albaali, Abdul Ghani & Alasis, Emil & Kaldellis, John K., 2016. "Experimental study on the effect of dust deposition on solar photovoltaic panels in desert environment," Renewable Energy, Elsevier, vol. 92(C), pages 499-505.
    5. Zhao, Weiping & Lv, Yukun & Zhou, Qingwen & Yan, Weiping, 2021. "Investigation on particle deposition criterion and dust accumulation impact on solar PV module performance," Energy, Elsevier, vol. 233(C).
    6. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    7. Al-Waeli, Ali H.A. & Kazem, Hussein A. & Yousif, Jabar H. & Chaichan, Miqdam T. & Sopian, K., 2020. "Mathematical and neural network modeling for predicting and analyzing of nanofluid-nano PCM photovoltaic thermal systems performance," Renewable Energy, Elsevier, vol. 145(C), pages 963-980.
    8. Moreira, M.O. & Balestrassi, P.P. & Paiva, A.P. & Ribeiro, P.F. & Bonatto, B.D., 2021. "Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. Ji, Ling & Liang, Xiaolin & Xie, Yulei & Huang, Guohe & Wang, Bing, 2021. "Optimal design and sensitivity analysis of the stand-alone hybrid energy system with PV and biomass-CHP for remote villages," Energy, Elsevier, vol. 225(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Yaw Marfo Missah & Fuseini Inusah & Ussiph Najim & Frimpong Twum, 2023. "Evaluating Agile Neural Educational System for Effective Resource Management," SAGE Open, , vol. 13(4), pages 21582440231, December.
    3. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Conceição, Ricardo & González-Aguilar, José & Merrouni, Ahmed Alami & Romero, Manuel, 2022. "Soiling effect in solar energy conversion systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    2. Song, Zhe & Liu, Jia & Yang, Hongxing, 2021. "Air pollution and soiling implications for solar photovoltaic power generation: A comprehensive review," Applied Energy, Elsevier, vol. 298(C).
    3. Kaiss, El-Cheikh Amer & Hassan, Noha M., 2024. "Optimizing the cleaning frequency of solar photovoltaic (PV) systems using numerical analysis and empirical models," Renewable Energy, Elsevier, vol. 228(C).
    4. Yao, Wanxiang & Kong, Xiangru & Xu, Ai & Xu, Puyan & Wang, Yan & Gao, Weijun, 2023. "New models for the influence of rainwater on the performance of photovoltaic modules under different rainfall conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    5. Karim Menoufi, 2017. "Dust Accumulation on the Surface of Photovoltaic Panels: Introducing the Photovoltaic Soiling Index (PVSI)," Sustainability, MDPI, vol. 9(6), pages 1-12, June.
    6. Carmen Otilia Rusănescu & Marin Rusănescu & Irina Aura Istrate & Gabriel Alexandru Constantin & Mihaela Begea, 2023. "The Effect of Dust Deposition on the Performance of Photovoltaic Panels," Energies, MDPI, vol. 16(19), pages 1-20, September.
    7. Umar, Shayan & Waqas, Adeel & Tanveer, Waqas & Shahzad, Nadia & Janjua, Abdul Kashif & Dehghan, Maziar & Qureshi, Muhammad Salik & Shakir, Sehar, 2023. "A building integrated solar PV surface-cleaning setup to optimize the electricity output of PV modules in a polluted atmosphere," Renewable Energy, Elsevier, vol. 216(C).
    8. Sahouane, Nordine & Ziane, Abderrezzaq & Dabou, Rachid & Neçaibia, Ammar & Rouabhia, Abdelkrim & Lachtar, Salah & Blal, Mohammed & Slimani, Abdeldjalil & Boudjamaa, Tidjar, 2023. "Technical and economic study of the sand and dust accumulation impact on the energy performance of photovoltaic system in Algerian Sahara," Renewable Energy, Elsevier, vol. 205(C), pages 142-155.
    9. Chanchangi, Yusuf N. & Ghosh, Aritra & Sundaram, Senthilarasu & Mallick, Tapas K., 2020. "Dust and PV Performance in Nigeria: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 121(C).
    10. Gowtham Vedulla & Anbazhagan Geetha & Ramalingam Senthil, 2022. "Review of Strategies to Mitigate Dust Deposition on Solar Photovoltaic Systems," Energies, MDPI, vol. 16(1), pages 1-28, December.
    11. Pankaj Borah & Leonardo Micheli & Nabin Sarmah, 2023. "Analysis of Soiling Loss in Photovoltaic Modules: A Review of the Impact of Atmospheric Parameters, Soil Properties, and Mitigation Approaches," Sustainability, MDPI, vol. 15(24), pages 1-26, December.
    12. Qaisieh, Alaa & Abu-Nabah, Bassam A. & Hamdan, Mohammad O. & Alami, Abdul Hai & Khanfar, Layla & Zaki, Laila, 2023. "Optical characterization of accumulated dust particles and the sustainability of transmitted solar irradiance to photovoltaic cells," Renewable Energy, Elsevier, vol. 219(P1).
    13. Dida, Mustapha & Boughali, Slimane & Bechki, Djamel & Bouguettaia, Hamza, 2020. "Output power loss of crystalline silicon photovoltaic modules due to dust accumulation in Saharan environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    14. István Bodnár & Dávid Matusz-Kalász & Ruben Rafael Boros, 2023. "Exploration of Solar Panel Damage and Service Life Reduction Using Condition Assessment, Dust Accumulation, and Material Testing," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    15. Gholami, Aslan & Saboonchi, Ahmad & Alemrajabi, Ali Akbar, 2017. "Experimental study of factors affecting dust accumulation and their effects on the transmission coefficient of glass for solar applications," Renewable Energy, Elsevier, vol. 112(C), pages 466-473.
    16. Yao, Wanxiang & Xu, Ai & Kong, Xiangru & Wang, Yan & Li, Xianli & Gao, Weijun, 2024. "Analysis of dust deposition law at the micro level and its impact on the annual performance of photovoltaic modules," Energy, Elsevier, vol. 306(C).
    17. Aritra Ghosh, 2020. "Soiling Losses: A Barrier for India’s Energy Security Dependency from Photovoltaic Power," Challenges, MDPI, vol. 11(1), pages 1-22, May.
    18. Li, Xianli & Zhang, Xinya & Wang, Zhiyuan & Wang, Changfeng & Yao, Wanxiang & Xu, Xin & Zheng, Shaojuan, 2022. "Dust accumulation effect of glazing cover inner surface on the performance of transpired solar air collector," Renewable Energy, Elsevier, vol. 195(C), pages 648-656.
    19. Eid Gul & Giorgio Baldinelli & Pietro Bartocci, 2022. "Energy Transition: Renewable Energy-Based Combined Heat and Power Optimization Model for Distributed Communities," Energies, MDPI, vol. 15(18), pages 1-18, September.
    20. Cui, Yiming & Liu, Mengmeng & Li, Wei & Lian, Jijian & Yao, Ye & Gao, Xifeng & Yu, Lina & Wang, Ting & Li, Yichu & Yin, Jilong, 2024. "An exploratory framework to identify dust on photovoltaic panels in offshore floating solar power stations," Energy, Elsevier, vol. 307(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4138-:d:831754. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.