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Cited article:

Deep Learning-Driven Solar Fault Detection in Solar–Hydrogen AIoT Systems: Implementing CNN VGG16, ResNet-50, DenseNet121, and EfficientNetB0 in a University-Based Framework

Salaki Reynaldo Joshua, Kenneth Yosua Palilingan, Salvius Paulus Lengkong and Sanguk Park
Hydrogen 7 (1) 1 (2025)
https://doi.org/10.3390/hydrogen7010001

Artificial-Intelligence-Based Detection of Defects and Faults in Photovoltaic Systems: A Survey

Ali Thakfan and Yasser Bin Salamah
Energies 17 (19) 4807 (2024)
https://doi.org/10.3390/en17194807

Fault classification using deep learning based model and impact of dust accumulation on solar photovoltaic modules

Rahma Aman, M. Rizwan and Astitva Kumar
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 45 (2) 4633 (2023)
https://doi.org/10.1080/15567036.2023.2205859