[1] Basnet, B., Chun, H. and Bang, J. (2020) 'An intelligent fault detection model for fault detection in photovoltaic
systems', Journal of Sensors, 2020, pp. 1–11. doi:10.1155/2020/6960328.
multiple
[2] He, Z., Chu, P., Li, C. et al. (2023) 'Compound fault diagnosis for photovoltaic arrays based on multi-label learning
considering
faults
coupling',
Energy
Conversion
and
Management,
279,
116742.
doi:10.1016/j.enconman.2023.116742.
[3] Akay, S.S., Özcan, O. and Yetemen, Ö. (2024) 'Efficiency analysis of solar farms by UAV-based thermal monitoring',
Engineering Science and Technology, an International Journal, 53, 101688. doi:10.1016/j.jestch.2024.101688.
[4] Pathak, S.P. and Patil, S.A. (2023) 'Evaluation of effect of pre-processing techniques in solar panel fault detection',
IEEE Access, 11, pp. 72848–60. doi:10.1109/ACCESS.2023.3293756.
[5] Balachandran, G.B., Devisridhivyadharshini, M., Ramachandran, M.E. et al. (2024) 'Comparative investigation of
imaging techniques, pre-processing and visual fault diagnosis using artificial intelligence models for solar photovoltaic
system: A comprehensive review', Measurement, 232, 114683. doi:10.1016/j.measurement.2024.114683.
[6] Hong, Y.-Y. and Pula, R.A. (2022) 'Methods of photovoltaic fault detection and classification: A review', Energy
Reports, 8, pp. 5898–929. doi:10.1016/j.egyr.2022.04.043.
[7] Ali, M.U., Khan, H.F., Masud, M. et al. (2020) 'A machine learning framework to identify the hotspot in photovoltaic
module using infrared thermography', Solar Energy, 208, pp. 643–51. doi:10.1016/j.solener.2020.08.027.
[8] Breiman, L. (2001) 'Random forests', Machine Learning, 45, pp. 5–32. doi:10.1023/A:1010933404324.
[9] Cortes, C. and Vapnik, V. (1995) 'Support-vector networks', Machine Learning, 20, pp. 273–97.
doi:10.1007/BF00994018.
[10] Cover, T. and Hart, P. (1967) 'Nearest neighbor pattern classification', IEEE Transactions on Information Theory,
13(1), pp. 21–7. doi:10.1109/TIT.1967.1053964.
[11] Quinlan, J.R. (1986) 'Induction of decision trees', Machine Learning, 1, pp. 81–106. doi:10.1007/BF00116251.
[12] Freund, Y. and Schapire, R.E. (1997) 'A decision-theoretic generalization of on-line learning and an application to
boosting', Journal of Computer and System Sciences, 55, pp. 119–39. doi:10.1006/jcss.1997.1504.
[13] Dalal, N. and Triggs, B. (2005) 'Histograms of oriented gradients for human detection', in Proceedings of the IEEE
Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–93.
doi:10.1109/CVPR.2005.177.
[14] Chen, T. and Guestrin, C. (2016) 'XGBoost: A scalable tree boosting system', in Proceedings of the 22nd ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–94.
doi:10.1145/2939672.2939785.
[15] Pathak, S.P., Patil, S. and Patel, S. (2022) 'Solar panel hotspot localization and fault classification using deep learning
approach', Procedia Computer Science, 204, pp. 698–705. doi:10.1016/j.procs.2022.08.084.
[16] Moradi Sizkouhi, A., Aghaei, M. and Esmailifar, S.M. (2021) 'A deep convolutional encoder-decoder architecture for
autonomous fault detection of PV plants using multi-copters', Solar Energy, 223, pp. 217–28.
doi:10.1016/j.solener.2021.05.029.
[17] Goyal, S. and Rajapakse, J.C. (2024) 'Self-supervised learning for hotspot detection and isolation from thermal
images', Expert Systems with Applications, 237, 121566. doi:10.1016/j.eswa.2023.121566.
[18] Noura, H.N., Chahine, K., Bassil, J. et al. (2025) 'Efficient combination of deep learning models for solar panel
damage and soiling detection', Measurement, 251, 117185. doi:10.1016/j.measurement.2025.117185.
unsupervised
[19] Oulefki, A., Himeur, Y., Trongtirakul, T. et al. (2024) 'Detection and analysis of deteriorated areas in solar PV modules
using
sensing
algorithms
and
3D augmented reality', Heliyon, 10, e27973.
doi:10.1016/j.heliyon.2024.e27973.
[20] Ying, Y., Ying, P., Men, H. et al. (2023) 'Image registration based fault localization in panoramas of mountain
mounted PV plants', Solar Energy, 256, pp. 16–31. doi:10.1016/j.solener.2023.03.049.
[21] Ghahremani, A., Adams, S.D., Norton, M. et al. (2025) 'Advancements in AI-driven detection and localisation of solar
panel defects', Advanced Engineering Informatics, 64, 103104. doi:10.1016/j.aei.2024.103104.
[22] American University of Sharjah (2023) Hotspot detection in solar panel dataset (Version 2) [Dataset]. Roboflow
Universe. Available at: https://universe.roboflow.com/american-university-of-sharjah/hotspot-detection-in-solar
panel/dataset/2 (Accessed: 16 March).
photovoltaic
[23] Tang, W., Yang, Q., Dai, Z. and Yan, W. (2024) 'Module defect detection and diagnosis for intelligent maintenance of
solar
plants:
doi:10.1016/j.energy.2024.131222.
Techniques,
systems
and
perspectives',
Energy,
297,
131222.