Mathematical and neural network modeling for predicting and analyzing of nanofluid-nano PCM photovoltaic thermal systems performance
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DOI: 10.1016/j.renene.2019.06.099
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Cited by:
- 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.
- 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).
- 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).
- 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.
- 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).
- 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).
- Salari, Ali & Shakibi, Hamid & Soleimanzade, Mohammad Amin & Sadrzadeh, Mohtada & Hakkaki-Fard, Ali, 2024. "Application of machine learning in evaluating and optimizing the hydrogen production performance of a solar-based electrolyzer system," Renewable Energy, Elsevier, vol. 220(C).
- 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).
- 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).
- 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).
- 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.
- 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).
- 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.
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Keywords
PV/T; Nano-sic; Nanofluid; Nano-PCM; Statistical analysis; Neural network;All these keywords.
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