This paper investigates the application of DenseNet121, a deep learning model, for automated fault detection in solar panels using Kangwon National University's Samcheok Campus as a case study. As solar energy plays a crucial role in sustainability efforts, ensuring the efficiency of solar panels is paramount. Traditional methods of fault detection are labor-intensive and prone to errors, necessitating more effective solutions. DenseNet121, leveraging its dense connectivity for feature learning from solar panel images, is explored in this study. Through rigorous training and validation, DenseNetl21 demonstrates high accuracy in identifying faults such as cracks, hotspots, and delamination. This research advances automated fault detection systems to optimize energy production and ensure long-term operational efficiency in solar installations. Future research directions include further optimizing DenseNet121, expanding datasets, and integrating real-time monitoring for enhanced reliability and cost-effectiveness. These advancements hold promise for transforming renewable energy technologies globally.