This paper explores the use of EfficientNetB0, a deep learning model, for automated fault detection in solar panels using Kangwon National University's Samcheok Campus as a case study. Given the critical role of solar energy in sustainability efforts, ensuring the efficiency of solar panels is crucial. Conventional fault detection methods are labor-intensive and prone to errors, necessitating more efficient solutions. EfficientNetB0, which utilizes efficient scaling methods to optimize model performance, is examined in this study. Through rigorous training and validation, EfficientNetB0 demonstrates high accuracy in detecting faults such as cracks, hotspots, and delamination in solar panel images. This research contributes to advancing automated fault detection systems aimed at optimizing energy production and ensuring sustained operational efficiency in solar installations. Future research directions include further refining EfficientNetB0, expanding datasets, and implementing real-time monitoring to enhance reliability and cost-effectiveness. These advancements hold significant potential for advancing renewable energy technologies on a global scale.