[1] Doyle, T., Erion‐Lorico, T., & Desharnais, R. (2020). PV module reliability scorecard. Relatório técnico, PVEL and DNV GL. [2] Seme, S., Sredenšek, K., Štumberger, B., & Hadžiselimović, M. (2019). Analysis of the performance of photovoltaic systems in Slovenia. Solar Energy, 180, 550-558.
[3] Quansah, D. A., & Adaramola, M. S. (2019). Assessment of early degradation and performance loss in five co-located solar photovoltaic module technologies installed in Ghana using performance ratio time-series regression. Renewable energy, 131, 900-910.
[4] Achour, Y., Berrada, A., Arechkik, A., & El Mrabet, R. (2023). Techno-economic assessment of hydrogen production from three different solar photovoltaic technologies. International Journal of Hydrogen Energy, 48(83), 32261-32276.
[5] Chiteka, K., & Enweremadu, C. (2025). A Review on Modeling and Prediction of Soiling on Solar Photovoltaics and Thermal Collectors. Journal of Solar Energy Research. 10(1), 2135-2160
[6] Ameur, A., Berrada, A., Bouaichi, A., & Loudiyi, K. (2022). Long-term performance and degradation analysis of different PV modules under temperate climate. Renewable Energy, 188, 37-51.
[7] Mohamed, M. (2022). Comparison between P&O and SSO techniques based MPPT algorithm for photovoltaic systems. International Journal of Electrical and Computer Engineering (IJECE).
doi.org/10.11591/ijece.v12i1
[8] Bayrak, F., Oztop, H. F., & Selimefendigil, F. (2020). Experimental study for the application of different cooling techniques in photovoltaic (PV) panels. Energy Conversion and Management, 212, 112789.
[9] Parsa, H. R., & Sarvi, M. (2024). Analysis of Intra-String Line-Line Fault in Photovoltaic System. Journal of Solar Energy Research, 9(1), 1780-1793.
[10] Ruiz-Gonzalez, R., Gomez-Gil, J., Gomez-Gil, F. J., & Martínez-Martínez, V. (2014). An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis. Sensors, 14(11), 20713-20735.
doi.org/10.3390/s141120713
[11] Hichem, H., & Djamel, B. (2015). Identification of Acoustics Microwaves (Bulk Acoustic Waves) in Piezoelectric Substrate (LiNbO3 Cut Y–Z) by Classification Using Neural Network. Journal of Nanoelectronics and Optoelectronics, 10(3), 314-319.
[12] Hafdaoui, H., Bouchakour, S., & Belhaouas, N. (2022). Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems. Journal of Renewable Energies, 25(2), 199-210.
[13] Hafdaoui, H., Bouchakour, S., & Belhaouas, N. (2022). Using machine learning for analysis a database outdoor monitoring of photovoltaic system. International Journal of Integrated Engineering, 14(6), 275-280.
doi.org/10.30880/ijie.2022.14.06.024
[14] Belhaouas, N., Hafdaoui, H., Nunzi, J. M., Khatir, S., Ernst, D., Mehareb, F., ... & Saheb-Koussa, D. (2025). Comprehensive analysis and insights into the relationship between temperature coefficients, PV failures, and investigating their correlation with other PV parameters. Solar Energy, 301, 113891.
[15] Herteleer, B., Huyck, B., Catthoor, F., Driesen, J., & Cappelle, J. (2017). Normalised efficiency of photovoltaic systems: Going beyond the performance ratio. Solar Energy, 157, 408-418.
[16] Hafdaoui, H., Belhaouas, N., Assem, H., Hadjrioua, F., & Madjoudj, N. (2023). Compare between the performance of different technologies of PV Modules using Artificial intelligence techniques. Journal of Renewable Energies, 99-106.
[17] Batiyah, S., Al-Subhi, A., Elsherbiny, O., Aldosari, O., & Aldawsari, M. (2025). Deep neural networks model for accurate photovoltaic parameter estimation under variable weather conditions. Solar Energy, 299, 113734.
[18] Lee, C. H., Lim, S. K., Park, S. J., & Kim, B. H. (2025). Photovoltaic Module Degradation Detection Using V–P Curve Derivatives and LSTM-Based Classification. Sensors, 25(20), 6475.
doi.org/10.3390/s25206475
[19] Ebied, M. A., Munshi, A., Alhuzali, S. A., El-Sotouhy, M. M., Shehta, A. I., & Elborlsy, M. S. (2025). Advanced deep learning modeling to enhance detection of defective photovoltaic cells in electroluminescence images. Scientific Reports, 15(1), 31640.
doi.org/10.1038/s41598-025-14478-y
[20] Song, Z., Xiao, F., Chen, Z., & Madsen, H. (2025). Probabilistic ultra-short-term solar photovoltaic power forecasting using natural gradient boosting with attention-enhanced neural networks. Energy and AI, 20, 100496.
[21] Hameed, M. A., Gottschalg, R., Scheer, R., & Alias, Q. M. (2025, February). Performance analysis with risk identifications and its economic impact on PV plant in harsh climates: Baghdad site case. In AIP Conference Proceedings (Vol. 3169, No. 1, p. 040136). AIP Publishing LLC.