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Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks

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  1. Rehman, Shafiqur & El-Amin, Ibrahim, 2012. "Performance evaluation of an off-grid photovoltaic system in Saudi Arabia," Energy, Elsevier, vol. 46(1), pages 451-458.
  2. Han, Youhua & Liu, Yang & Lu, Shixiang & Basalike, Pie & Zhang, Jili, 2021. "Electrical performance and power prediction of a roll-bond photovoltaic thermal array under dewing and frosting conditions," Energy, Elsevier, vol. 237(C).
  3. Marcos A. Ponce-Jara & Ivan Pazmino & Ángelo Moreira-Espinoza & Alfonso Gunsha-Morales & Catalina Rus-Casas, 2024. "Assessment of Single-Axis Solar Tracking System Efficiency in Equatorial Regions: A Case Study of Manta, Ecuador," Energies, MDPI, vol. 17(16), pages 1-19, August.
  4. Pantic, Lana S. & Pavlović, Tomislav M. & Milosavljević, Dragana D. & Radonjic, Ivana S. & Radovic, Miodrag K. & Sazhko, Galina, 2016. "The assessment of different models to predict solar module temperature, output power and efficiency for Nis, Serbia," Energy, Elsevier, vol. 109(C), pages 38-48.
  5. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
  6. 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.
  7. Abubaker, Ahmad M. & Darwish Ahmad, Adnan & Salaimeh, Ahmad A. & Akafuah, Nelson K. & Saito, Kozo, 2022. "A novel solar combined cycle integration: An exergy-based optimization using artificial neural network," Renewable Energy, Elsevier, vol. 181(C), pages 914-932.
  8. De Giorgi, M.G. & Malvoni, M. & Congedo, P.M., 2016. "Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine," Energy, Elsevier, vol. 107(C), pages 360-373.
  9. Senturk, Ali, 2020. "Investigation of datasheet provided temperature coefficients of photovoltaic modules under various sky profiles at the field by applying a new validation procedure," Renewable Energy, Elsevier, vol. 152(C), pages 644-652.
  10. Katsikogiannis, Odysseas Alexandros & Ziar, Hesan & Isabella, Olindo, 2022. "Integration of bifacial photovoltaics in agrivoltaic systems: A synergistic design approach," Applied Energy, Elsevier, vol. 309(C).
  11. Sommerfeldt, Nelson & Madani, Hatef, 2017. "Revisiting the techno-economic analysis process for building-mounted, grid-connected solar photovoltaic systems: Part one – Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1379-1393.
  12. Muñoz, J.V. & Nofuentes, G. & Fuentes, M. & de la Casa, J. & Aguilera, J., 2016. "DC energy yield prediction in large monocrystalline and polycrystalline PV plants: Time-domain integration of Osterwald's model," Energy, Elsevier, vol. 114(C), pages 951-960.
  13. Njoku, H.O., 2016. "Upper-limit solar photovoltaic power generation: Estimates for 2-axis tracking collectors in Nigeria," Energy, Elsevier, vol. 95(C), pages 504-516.
  14. Kiraly, Annamaria & Pahor, Bojan & Kravanja, Zdravko, 2013. "Achieving energy self-sufficiency by integrating renewables into companies' supply networks," Energy, Elsevier, vol. 55(C), pages 46-57.
  15. 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.
  16. Dias, César Luiz de Azevedo & Castelo Branco, David Alves & Arouca, Maurício Cardoso & Loureiro Legey, Luiz Fernando, 2017. "Performance estimation of photovoltaic technologies in Brazil," Renewable Energy, Elsevier, vol. 114(PB), pages 367-375.
  17. 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.
  18. Almonacid, F. & Fernández, E.F. & Mallick, T.K. & Pérez-Higueras, P.J., 2015. "High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature," Energy, Elsevier, vol. 84(C), pages 336-343.
  19. Lujano-Rojas, Juan M. & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2013. "Probabilistic modelling and analysis of stand-alone hybrid power systems," Energy, Elsevier, vol. 63(C), pages 19-27.
  20. Saber, Esmail M. & Lee, Siew Eang & Manthapuri, Sumanth & Yi, Wang & Deb, Chirag, 2014. "PV (photovoltaics) performance evaluation and simulation-based energy yield prediction for tropical buildings," Energy, Elsevier, vol. 71(C), pages 588-595.
  21. Zhang, Ning & Sun, Qiuye & Yang, Lingxiao, 2021. "A two-stage multi-objective optimal scheduling in the integrated energy system with We-Energy modeling," Energy, Elsevier, vol. 215(PB).
  22. Adewole, Bamiji Z. & Abidakun, Olatunde A. & Asere, Abraham A., 2013. "Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner," Energy, Elsevier, vol. 61(C), pages 606-611.
  23. 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.
  24. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
  25. Torres-Ramírez, M. & Elizondo, D. & García-Domingo, B. & Nofuentes, G. & Talavera, D.L., 2015. "Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology," Energy, Elsevier, vol. 86(C), pages 323-334.
  26. Pérez-Alonso, J. & Pérez-García, M. & Pasamontes-Romera, M. & Callejón-Ferre, A.J., 2012. "Performance analysis and neural modelling of a greenhouse integrated photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4675-4685.
  27. Kljajić, Miroslav & Gvozdenac, Dušan & Vukmirović, Srdjan, 2012. "Use of Neural Networks for modeling and predicting boiler's operating performance," Energy, Elsevier, vol. 45(1), pages 304-311.
  28. Paulescu, Marius & Badescu, Viorel & Dughir, Ciprian, 2014. "New procedure and field-tests to assess photovoltaic module performance," Energy, Elsevier, vol. 70(C), pages 49-57.
  29. 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.
  30. Jesús Polo & Nuria Martín-Chivelet & Carlos Sanz-Saiz, 2022. "BIPV Modeling with Artificial Neural Networks: Towards a BIPV Digital Twin," Energies, MDPI, vol. 15(11), pages 1-11, June.
  31. Maatallah, Taher & El Alimi, Souheil & Nassrallah, Sassi Ben, 2011. "Performance modeling and investigation of fixed, single and dual-axis tracking photovoltaic panel in Monastir city, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 4053-4066.
  32. Bizon, Nicu, 2013. "Energy harvesting from the PV Hybrid Power Source," Energy, Elsevier, vol. 52(C), pages 297-307.
  33. Kim, Ik-Pyo & Hwang, Ha Sung & Jung, Jin-Woo, 2018. "Conflict cause analysis between stakeholders in a utility-scale PV plant and its policy improvement methods in Korea," Energy Policy, Elsevier, vol. 121(C), pages 452-463.
  34. Atlam, Ozcan & Kolhe, Mohan, 2013. "Performance evaluation of directly photovoltaic powered DC PM (direct current permanent magnet) motor – propeller thrust system," Energy, Elsevier, vol. 57(C), pages 692-698.
  35. Chine, W. & Mellit, A. & Pavan, A. Massi & Kalogirou, S.A., 2014. "Fault detection method for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 66(C), pages 99-110.
  36. Abdul Rauf Bhatti & Ahmed Bilal Awan & Walied Alharbi & Zainal Salam & Abdullah S. Bin Humayd & Praveen R. P. & Kankar Bhattacharya, 2021. "An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
  37. Ke Shi & Chuangyi Li & Choongwan Koo, 2021. "A Techno-Economic Feasibility Analysis of Mono-Si and Poly-Si Photovoltaic Systems in the Rooftop Area of Commercial Building under the Feed-In Tariff Scheme," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
  38. 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.
  39. Mikulandrić, Robert & Lončar, Dražen & Cvetinović, Dejan & Spiridon, Gabriel, 2013. "Improvement of existing coal fired thermal power plants performance by control systems modifications," Energy, Elsevier, vol. 57(C), pages 55-65.
  40. Kadri, Riad & Andrei, Horia & Gaubert, Jean-Paul & Ivanovici, Traian & Champenois, Gérard & Andrei, Paul, 2012. "Modeling of the photovoltaic cell circuit parameters for optimum connection model and real-time emulator with partial shadow conditions," Energy, Elsevier, vol. 42(1), pages 57-67.
  41. 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.
  42. Jihoon Jang & Joosang Lee & Eunjo Son & Kyungyong Park & Gahee Kim & Jee Hang Lee & Seung-Bok Leigh, 2019. "Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection," Energies, MDPI, vol. 12(21), pages 1-20, November.
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