Detection of faulty solar panels using artificial intellegence methods

Solar energy is gaining popularity as a renewable energy source, but maintaining the efficiency of solar panels poses challenges due to potential defects that can lead to significant energy production losses. The objective of this thesis is to develop an accurate and robust classification model using deep learning techniques to detect faulty solar panels. The proposed method relies on Convolutional Neural Networks (CNNs) and transfer learning techniques to achieve high accuracy, reaching 93% in accurately classifying faulty solar panels using the proposed CNN model. The transfer learning architectures are DenseNet121, ResNet50, MobileNetV2, and Xception models, were implemented with accuracies of 81.66%, 51.21%, 77.47%, and 90.31% respectively. This study demonstrates the effectiveness of employing deep learning techniques alongside suitable data preprocessing methods to develop the classification of defective solar panels.