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Mathematical and neural network modeling for predicting and analyzing of nanofluid-nano PCM photovoltaic thermal systems performance

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  • Al-Waeli, Ali H.A.
  • Kazem, Hussein A.
  • Yousif, Jabar H.
  • Chaichan, Miqdam T.
  • Sopian, K.

Abstract

This paper aims to enhance the power production performance of the PV/T based on three cooling models using nanofluid, SiC-water and nano-PCM. The effect of solar irradiance and ambient temperature on the power productivity performance has been investigated based on sensitivity analysis that shows that electrical current and solar irradiance has more impact on power prediction than voltage and temperature. Also, three mathematical linear prediction models were developed and compared with the prediction of ANN models, and the results were verified and fit the experimental results. The comparison with published literature is made based on three common evaluation criteria that include the coefficient of determination R2, MSE and RSME. The proposed predicting models attained an excellent R2 result of 0.99 and MSE value of 0.006 and RSME of 0.009 for both P-M1 and P-M2 models. Besides, the P-M3 obtained an MSE value of 0.022. Finally, the proposed linear prediction models help to reduce the error in furcating future results and determine the best conditions for any solar system in an easy and fast way.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:963-980
    DOI: 10.1016/j.renene.2019.06.099
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    Citations

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    Cited by:

    1. 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.
    2. Shahsavar, Amin & Jha, Prabhakar & Arici, Muslum & Kefayati, Gholamreza, 2021. "A comparative experimental investigation of energetic and exergetic performances of water/magnetite nanofluid-based photovoltaic/thermal system equipped with finned and unfinned collectors," Energy, Elsevier, vol. 220(C).
    3. Yu, Qinghua & Chen, Xi & Yang, Hongxing, 2021. "Research progress on utilization of phase change materials in photovoltaic/thermal systems: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    4. Hussein A. Kazem, 2023. "Prediction of grid-connected photovoltaic performance using artificial neural networks and experimental dataset considering environmental variation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(3), pages 2857-2884, March.
    5. Wang, Yunjie & Yang, Huihan & Chen, Haifei & Yu, Bendong & Zhang, Haohua & Zou, Rui & Ren, Shaoyang, 2023. "A review: The development of crucial solar systems and corresponding cooling technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    6. Cui, Yuanlong & Zhu, Jie & Zhang, Fan & Shao, Yiming & Xue, Yibing, 2022. "Current status and future development of hybrid PV/T system with PCM module: 4E (energy, exergy, economic and environmental) assessments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    7. Karami, Babak & Azimi, Neda & Ahmadi, Shahin, 2021. "Increasing the electrical efficiency and thermal management of a photovoltaic module using expanded graphite (EG)/paraffin-beef tallow-coconut oil composite as phase change material," Renewable Energy, Elsevier, vol. 178(C), pages 25-49.
    8. Ma, Ting & Guo, Zhixiong & Lin, Mei & Wang, Qiuwang, 2021. "Recent trends on nanofluid heat transfer machine learning research applied to renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    9. Shi, Lei & Zhang, Shuai & Arshad, Adeel & Hu, Yanwei & He, Yurong & Yan, Yuying, 2021. "Thermo-physical properties prediction of carbon-based magnetic nanofluids based on an artificial neural network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    10. Ahmad Manasrah & Mohammad Masoud & Yousef Jaradat & Piero Bevilacqua, 2022. "Investigation of a Real-Time Dynamic Model for a PV Cooling System," Energies, MDPI, vol. 15(5), pages 1-15, March.
    11. Reji Kumar, R. & Samykano, M. & Pandey, A.K. & Kadirgama, K. & Tyagi, V.V., 2020. "Phase change materials and nano-enhanced phase change materials for thermal energy storage in photovoltaic thermal systems: A futuristic approach and its technical challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    12. Rostami, Sara & Afrand, Masoud & Shahsavar, Amin & Sheikholeslami, M. & Kalbasi, Rasool & Aghakhani, Saeed & Shadloo, Mostafa Safdari & Oztop, Hakan F., 2020. "A review of melting and freezing processes of PCM/nano-PCM and their application in energy storage," Energy, Elsevier, vol. 211(C).

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