Open Access
| Issue |
EPJ Web Conf.
Volume 371, 2026
9th International Congress on Thermal Sciences (AMT’2026)
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 17 | |
| Section | Renewable Energy and Clean Technologies | |
| DOI | https://doi.org/10.1051/epjconf/202637103001 | |
| Published online | 22 May 2026 | |
- B. Li, C. Delpha, D. Diallo, A. Migan-Dubois, Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review. Renew. Sust. Energ. Rev. 138, 110512 (2021). https://doi.org/10.1016/j.rser.2020.110512 [Google Scholar]
- J.A. Tsanakas, L. Ha, C. Buerhop, Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges. Renew. Sust. Energ. Rev. 62, 695-709 (2016). https://doi.org/10.1016/j.rser.2016.04.079 [Google Scholar]
- S.K. Firth, K.J. Lomas, S.J. Rees, A simple model of PV system performance and its use in fault detection. Sol. Energy 84, 624-635 (2010). https://doi.org/10.1016/j.solener.2009.08.004 [Google Scholar]
- S. Naveen Venkatesh, V. Sugumaran, Fault Detection in aerial images of photovoltaic modules based on Deep learning. IOP Conf. Ser. Mater. Sci. Eng. 1012, 012030 (2021). https://doi.org/10.1088/1757-899x/1012/1/012030 [Google Scholar]
- P. Guerriero, G. Cuozzo, S. Daliento, Health diagnostics of PV panels by means of single cell analysis of thermographic images, in Proceedings of the EEEIC conference (2016) [Google Scholar]
- Y. Zefri, A. Elkettani, I. Sebari, S.A. Lamallam, Thermal infrared and visual inspection of photovoltaic installations by uav photogrammetry—application case: Morocco. Drones 2, 1-24 (2018) [Google Scholar]
- U. Pruthviraj, Y. Kashyap, E. Baxevanaki, P. Kosmopoulos, [Insert title of the article here]. Remote Sens. 15 (2023) [Google Scholar]
- W.H. Maes, A.R. Huete, K. Steppe, Optimizing the processing of UAV-based thermal imagery. Remote Sens. 9 (2017). https://doi.org/10.3390/rs9050476 [Google Scholar]
- H. Hoffmann, H. Nieto, R. Jensen, R. Guzinski, P. Zarco-Tejada, T. Friborg, Estimating evaporation with thermal UAV data and two-source energy balance models. Hydrol. Earth Syst. Sci. 20, 697-713 (2016). https://doi.org/10.5194/hess-20-697-2016 [Google Scholar]
- Y. Liu, S. Liu, Z. Wang, A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion 24, 147-164 (2015). https://doi.org/10.1016/j.inffus.2014.09.004 [Google Scholar]
- Y. Zefri, I. Sebari, H. Hajji, G. Aniba, In-depth investigation of applied digital photogrammetry to imagery-based RGB and thermal infrared aerial inspection of largescale photovoltaic installations. Remote Sens. Appl. 23 (2021) doi: 10.1016/j.rsase.2021.100576 [Google Scholar]
- B.S. Krishnan et al., Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys. Sci. Rep. 13 (2023), doi: 10.1038/s41598-023-37295-7 [Google Scholar]
- J. Ma, C. Chen, C. Li, J. Huang, Infrared and visible image fusion via gradient transfer and total variation minimization. Information Fusion 31, 100-109 (2016). https://doi.org/10.1016/j.inffus.2016.02.001 [Google Scholar]
- D.P. Bavirisetti, G. Xiao, J. Zhao, R. Dhuli, G. Liu, Multi-scale Guided Image and Video Fusion: A Fast and Efficient Approach. Circuits Syst. Signal Process. 38, 5576-5605 (2019). https://doi.org/10.1007/s00034-019-01131-z [Google Scholar]
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