## 6 Conclusions
These results indicate that the model's predictions were generally accurate, with errors averaging around 1685 units. However, the relatively high MSE suggests that there are cases where the model's predictions deviate significantly from actual values, highlighting areas where further refinements are needed to improve accuracy. The research also investigated the use of Convolutional Neural Networks (CNNs) and ResNet-50 for fault detection in solar panels. These deep learning models are highly effective in processing and analyzing thermographic images of solar panels. The CNN model, based on the VGG-16 architecture, achieved an accuracy of 80% but with a high loss value of 78%, indicating some challenges in fault detection. In comparison, the ResNet-50 model demonstrated superior performance, achieving 85% accuracy with a significantly reduced loss value of 42%. This highlights ResNet-50's better ability to capture complex patterns in the data, leading to more accurate detection of solar panel defects. Despite this success, further optimization is required to reduce prediction errors and enhance the model's capacity to identify subtle defects. Several key discoveries emerged from the analysis of these models. The Transformer model's ability to predict energy output shows its potential for optimizing solar-hydrogen systems. However, the model's relatively high error rates in certain cases suggest that additional improvements are necessary, particularly to increase its accuracy in more challenging scenarios. The comparison of ResNet-50 with VGG-16 demonstrated that ResNet-50 is more suitable for fault detection tasks, given its higher accuracy and lower loss values. Nonetheless, further development is needed to improve the model's performance in detecting minor defects that may not be immediately apparent. Building on the results of this study, several avenues for future research can further improve the effectiveness and reliability of solar-hydrogen hybrid systems. First, integrating more comprehensive environmental data--such as real-time weather reports or air quality measurements--could enhance the Transformer model's accuracy by reducing prediction errors and improving its ability to account for fluctuations in solar energy production. Second, fault detection models could be improved by exploring alternative deep learning architectures or incorporating additional data sources, such as vibration sensors or acoustic emissions. Using ensemble learning techniques, which combine multiple models to increase overall performance, could also strengthen fault detection. Finally, expanding the use of AI and IoT technologies could lead to the development of a fully automated monitoring and maintenance system. Such a system would enable real-time issue detection and correction, minimizing downtime and maximizing energy production. Predictive analytics could also be used to optimize energy usage and storage, ensuring that the university's energy infrastructure operates at maximum efficiency at all times. This study highlights the potential of integrating AI, IoT, and machine learning into solar-hydrogen systems to improve renewable energy management in academic institutions. The Transformer model demonstrated potential in energy output prediction, while ResNet-50 showed superior performance in fault detection. However, there remains room for improvement in both models. Future research should focus on refining these models, incorporating more data sources, and exploring the scalability of solar-hydrogen systems in diverse settings. Through continued advancements in technology, institutions can contribute to the global transition to sustainable energy sources. S.R.J. : project evaluation, methodology, investigation, resources, supervision, modeling, simulation. A.N.Y. : data analysis, investigation. S.P. : software development, functionality evaluation. K.K. : conceptualization, funding acquisition, resources, supervision, writing--original draft, writing--review and editing.