## 4 Results and Discussion
### Fault Detection Performance  
Reduced energy production results from trash accumulation on solar panels, including dust, snow, bird droppings, and other debris. This reduces the panels' capacity to convert sunlight into energy. To ensure that solar panels remain efficient, regular monitoring and cleaning are crucial. Implementing a systematic monitoring and cleaning procedure is essential for optimizing resource use, lowering maintenance costs, and enhancing the efficiency of the panels. By establishing a well-planned routine for monitoring and cleaning, solar panel owners can maximize energy production, extend the lifespan of their panels, and contribute to broader sustainability goals. The goal of this dataset is to assess how well different machine-learning classifiers detect dust, snow, bird droppings, and mechanical and electrical problems on solar panel surfaces. Six different class folders are included in the dataset for classification: mechanical damage, electrical damage, snow, bird droppings, garbage, and dirt (Figure 5). There is a small imbalance in the quantity of photos gathered, because the data are taken from the internet. Figure 4: Comparison of actual and predicted solar hydrogen energy production using the Transformer model. To ensure the integrity and quality of the dataset used for training a machine learning model, several steps are involved in the data verification process. Preparing the data for training requires performing pretreatment tasks, such as cleaning, normalization, and feature engineering. It is important to review the preprocessed data for any anomalies, inconsistencies, or missing variables that could affect the model's performance. This may include visualizing the data using plots or graphs to identify patterns or outliers. Additionally, addressing class imbalances where certain classes are overrepresented or underrepresented is critical to mitigate potential biases in the model. It is critical to divide the dataset into training and validation sets in order to evaluate the model's performance on untested data. This guarantees the robustness and generalizability of the model by enabling the use of cross-validation procedures. Maintaining the model's accuracy and efficacy over time also depends on constant observation and updating of the training data as new information becomes available. Machine learning practitioners can create more accurate and dependable models for a variety of applications, such as solar panel fault detection, by carefully analyzing the training data. The results of defect detection models applied to solar panel images reveal differing levels of performance between two architectures, VGG-16 and ResNet-50, as presented in the \"Prediction Results\" section (Figure 6). The model based on the VGG-16 architecture achieved an accuracy of 80% with a loss value of 78%. This indicates that while the model correctly identified 80% of instances, there is still a significant gap between the predicted and actual labels, reflected in the higher loss value. Conversely, the ResNet-50-based model demonstrated improved performance, with an accuracy of 85% and a reduced loss value of 42%. This suggests that the ResNet-50 model is better at identifying patterns and features in solar panel images, leading to more accurate predictions and a smaller prediction error. Despite these advances, there remains room for further optimization to minimize errors and enhance the precision of fault detection in solar panels. Overall, both models show promising results in identifying defects, with ResNet-50 outperforming VGG-16 in accuracy and loss metrics. Figure 5: Training data. Examining flaw detection models on solar panel photos reveals the relative advantages and drawbacks of two well-known architectures: VGG-16 and ResNet-50 (Figure 7). With an accuracy of 80%, the VGG-16 model demonstrated a respectable capacity to accurately detect solar panel flaws.