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Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods

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  1. Voyant, Cyril & Paoli, Christophe & Muselli, Marc & Nivet, Marie-Laure, 2013. "Multi-horizon solar radiation forecasting for Mediterranean locations using time series models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 44-52.
  2. Piliougine, Michel & Elizondo, David & Mora-López, Llanos & Sidrach-de-Cardona, Mariano, 2013. "Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules," Applied Energy, Elsevier, vol. 112(C), pages 610-617.
  3. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
  4. Ma, Tao & Yang, Hongxing & Lu, Lin, 2014. "Solar photovoltaic system modeling and performance prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 304-315.
  5. Nawal Rai & Amel Abbadi & Fethia Hamidia & Nadia Douifi & Bdereddin Abdul Samad & Khalid Yahya, 2023. "Biogeography-Based Teaching Learning-Based Optimization Algorithm for Identifying One-Diode, Two-Diode and Three-Diode Models of Photovoltaic Cell and Module," Mathematics, MDPI, vol. 11(8), pages 1-30, April.
  6. Manuel Angel Gadeo-Martos & Antonio Jesús Yuste-Delgado & Florencia Almonacid Cruz & Jose-Angel Fernandez-Prieto & Joaquin Canada-Bago, 2019. "Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems," Energies, MDPI, vol. 12(3), pages 1-22, February.
  7. Guerrero-Rodríguez, N.F. & Rey-Boué, Alexis B. & Bueno, E.J. & Ortiz, Octavio & Reyes-Archundia, Enrique, 2017. "Synchronization algorithms for grid-connected renewable systems: Overview, tests and comparative analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 629-643.
  8. Martha Lucia Orozco-Gutierrez, 2020. "An Interval-Arithmetic-Based Approach to the Parametric Identification of the Single-Diode Model of Photovoltaic Generators," Energies, MDPI, vol. 13(4), pages 1-22, February.
  9. García-Domingo, B. & Piliougine, M. & Elizondo, D. & Aguilera, J., 2015. "CPV module electric characterisation by artificial neural networks," Renewable Energy, Elsevier, vol. 78(C), pages 173-181.
  10. Sampath Kumar Vankadara & Shamik Chatterjee & Praveen Kumar Balachandran & Lucian Mihet-Popa, 2022. "Marine Predator Algorithm (MPA)-Based MPPT Technique for Solar PV Systems under Partial Shading Conditions," Energies, MDPI, vol. 15(17), pages 1-16, August.
  11. Yousri, Dalia & Thanikanti, Sudhakar Babu & Allam, Dalia & Ramachandaramurthy, Vigna K. & Eteiba, M.B., 2020. "Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters," Energy, Elsevier, vol. 195(C).
  12. Yadav, Amit Kumar & Chandel, S.S., 2017. "Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 955-969.
  13. Ayman M. Mansour & Abdulaziz Almutairi & Saeed Alyami & Mohammad A. Obeidat & Dhafer Almkahles & Jagabar Sathik, 2021. "A Unique Unified Wind Speed Approach to Decision-Making for Dispersed Locations," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
  14. Wang, Gang & Zhao, Ke & Shi, Jiangtao & Chen, Wei & Zhang, Haiyang & Yang, Xinsheng & Zhao, Yong, 2017. "An iterative approach for modeling photovoltaic modules without implicit equations," Applied Energy, Elsevier, vol. 202(C), pages 189-198.
  15. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
  16. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
  17. Wang, Gang & Zhao, Ke & Qiu, Tian & Yang, Xinsheng & Zhang, Yong & Zhao, Yong, 2016. "The error analysis of the reverse saturation current of the diode in the modeling of photovoltaic modules," Energy, Elsevier, vol. 115(P1), pages 478-485.
  18. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
  19. Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Pérez-Higueras, P.J., 2013. "Models for the electrical characterization of high concentration photovoltaic cells and modules: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 752-760.
  20. Chen, Zhicong & Yu, Hui & Luo, Linlu & Wu, Lijun & Zheng, Qiao & Wu, Zhenhui & Cheng, Shuying & Lin, Peijie, 2021. "Rapid and accurate modeling of PV modules based on extreme learning machine and large datasets of I-V curves," Applied Energy, Elsevier, vol. 292(C).
  21. Samuel R. Fahim & Hany M. Hasanien & Rania A. Turky & Shady H. E. Abdel Aleem & Martin Ćalasan, 2022. "A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction," Energies, MDPI, vol. 15(23), pages 1-56, November.
  22. Jordehi, A. Rezaee, 2016. "Parameter estimation of solar photovoltaic (PV) cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 354-371.
  23. Guerrero-Rodríguez, N.F. & Rey-Boué, Alexis B. & Herrero-de Lucas, Luis C. & Martinez-Rodrigo, Fernando, 2015. "Control and synchronization algorithms for a grid-connected photovoltaic system under harmonic distortions, frequency variations and unbalances," Renewable Energy, Elsevier, vol. 80(C), pages 380-395.
  24. Almonacid, F. & Rus, C. & Pérez-Higueras, P. & Hontoria, L., 2011. "Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks," Energy, Elsevier, vol. 36(1), pages 375-384.
  25. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.
  26. Boutana, N. & Mellit, A. & Lughi, V. & Massi Pavan, A., 2017. "Assessment of implicit and explicit models for different photovoltaic modules technologies," Energy, Elsevier, vol. 122(C), pages 128-143.
  27. Karabacak, Kerim & Cetin, Numan, 2014. "Artificial neural networks for controlling wind–PV power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 804-827.
  28. Fernández, Eduardo F. & Almonacid, Florencia, 2014. "Spectrally corrected direct normal irradiance based on artificial neural networks for high concentrator photovoltaic applications," Energy, Elsevier, vol. 74(C), pages 941-949.
  29. Torres-Ramírez, M. & Nofuentes, G. & Silva, J.P. & Silvestre, S. & Muñoz, J.V., 2014. "Study on analytical modelling approaches to the performance of thin film PV modules in sunny inland climates," Energy, Elsevier, vol. 73(C), pages 731-740.
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