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All issues Volume 363 (2026) EPJ Web Conf., 363 (2026) 01019 References
Open Access
Issue
EPJ Web Conf.
Volume 363, 2026
International Conference on Low-Carbon Development and Materials for Solar Energy (ICLDMS’26)
Article Number 01019
Number of page(s) 22
Section Energy Materials
DOI https://doi.org/10.1051/epjconf/202636301019
Published online 16 April 2026
  1. F Alpsalaz, Y. Ozupak, E. Aslan, and H. Uzel, “Hybrid Machine Learning Approach for Enhanced Fault Detection and Power Estimation in Photovoltaic Systems,” IET Renewable Power Generation, vol. 20, no. 1, 2026, doi: 10.1049/rpg2.70153. [Google Scholar]
  2. V. Khandeparkar and S. K. Senthil Kumar, “Effectiveness of supervised machine learning models for electrical fault detection in solar PV systems,” Sci. Rep., vol. 15, no. 1, 2025, doi: 10.1038/s41598-025-18802-4. [Google Scholar]
  3. Y. Ozupak, “Real-time detection of photovoltaic module faults using a hybrid machine learning model,” Solar Energy, vol. 302, 2025, doi: 10.1016/j.solener.2025.114014. [Google Scholar]
  4. M. V Prashanth et al., “Machine Learning Approaches for Solar PV Fault Identification,” SN Comput. Sci., vol. 6, no. 7, 2025, doi: 10.1007/s42979-025-04364-9. [Google Scholar]
  5. M. Parvin, H. Yousefi, and B. Mohammadi-ivatloo, “Novel stacking algorithm with feature extraction for photovoltaic fault classification,” Solar Energy, vol. 305, 2026, doi: 10.1016/j.solener.2025.114270. [Google Scholar]
  6. A. Nedaei, A. Eskandari, and M. Aghaei, “Photovoltaic fault detection and classification: Reconsideration of classic machine learning and dataset shrinkage techniques for simplification,” Results in Engineering, vol. 27, 2025, doi: 10.1016/j.rineng.2025.106356. [Google Scholar]
  7. S. Tufail and A. I. I Sarwat, “A Comparative Study of Dimensionality Reduction Methods for Accurate and Efficient Inverter Fault Detection in Grid-Connected Solar Photovoltaic Systems,” Electronics (Switzerland), vol. 14, no. 14, 2025, doi: 10.3390/electronics14142916. [Google Scholar]
  8. M. Abdelsattar, A. AbdelMoety, and A. Emad-Eldeen, “Advanced machine learning techniques for predicting power generation and fault detection in solar photovoltaic systems,” Neural Comput. Appl., vol. 37, no. 15, pp. 8825–8844, 2025, doi: 10.1007/s00521-025-11035-6. [Google Scholar]
  9. A. Namoune, A. Chaker, and I. Saouane, “A dual-approach machine learning and deep learning framework for enhanced fault detection in photovoltaic systems: Incorporating SDM parameter analysis and thermal imaging,” Journal of Renewable and Sustainable Energy, vol. 17, no. 3, 2025, doi: 10.1063/5.0264116. [Google Scholar]
  10. G. Nassreddine, A. Arid, M. Nassereddine, and O. Al-Khatib, “Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques,” Applied AI Letters, vol. 6, no. 2, 2025, doi: 10.1002/ail2.115. [Google Scholar]
  11. K. Ratsheola, D. Setlhaolo, A. Rasool, A. Ali, and N. E. Mabunda, “A Hybrid Artificial Intelligence for Fault Detection and Diagnosis of Photovoltaic Systems Using Autoencoders and Random Forests Classifiers,” Eng, vol. 6, no. 10, 2025, doi: 10.3390/eng6100254. [Google Scholar]
  12. M. S. Hassan, V. J. Chin, and L. Gopal, “Accurate diagnosis of concurrent faults in photovoltaic systems using CONMI-based feature selection and Support vector machines,” Energy Convers. Manag., vol. 344, 2025, doi: 10.1016/j.enconman.2025.120293. [Google Scholar]
  13. M. Parvin, H. Yousefi, and B. Mohammadi-ivatloo, “Photovoltaic fault detection algorithm using ensemble learning enhanced with deep neural network feature engineering,” Results in Engineering, vol. 27, 2025, doi: 10.1016/j.rineng.2025.106491. [Google Scholar]
  14. M. S. Ibrahim, H. K. Almulla, A. D. Sallibi, A. A. Nafea, A. K. Kareem, and K. M. Ali Alheeti, “Enhanced fault detection in photovoltaic systems using an ensemble machine learning approach,” International Journal of Reconfigurable and Embedded Systems, vol. 14, no. 2, pp. 507–517, 2025, doi: 10.11591/ijres.v14.i2.pp507-517. [Google Scholar]
  15. A. Freej, A. S. Sabik, and I. A. Nassar, “Performance Improvement of Photovoltaic Panels Through Advanced Fault Detection Techniques,” Processes, vol. 13, no. 12, 2025, doi: 10.3390/pr13123831. [Google Scholar]

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