## 6 Conclusions  
This study focused on integrating advanced technologies such as artificial intelligence (AI), machine learning, and the Internet of Things (IoT) into a solar-hydrogen system at Kangwon National University's Samcheok campus. The primary goal was to enhance the efficiency, reliability, and sustainability of renewable energy systems, particularly in an academic setting. The findings offer valuable insights into the application of data collection, predictive modeling, and fault detection techniques to improve solar energy output and storage while ensuring the long-term performance of solar panels. The integration of solar-hydrogen systems represents a significant advancement in the pursuit of sustainable energy solutions. These systems combine solar energy generation with hydrogen production and storage to address the intermittent nature of solar power, resulting in a flexible and reliable energy source. The Green Energy Building at the university's Samcheok campus serves as an effective demonstration of how such technologies can be implemented in a real-world setting. This solar-hydrogen system increases the campus's energy independence, offering a cleaner alternative to fossil fuels. Even during periods of low solar irradiance, the system provides a stable and reliable energy supply by storing excess energy as hydrogen. This setup reduces the university's carbon footprint, decreases reliance on external energy sources, and offers a model for other institutions seeking to adopt similar technologies. Accurate data collection is crucial for optimizing any energy management system. This study gathered solar energy data from various sources, including sensor data from solar panels, solar irradiance measurements, and historical weather data. This information \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No** & **Aspect** & **Details** \\ \hline
1 & Real-Time Monitoring & Continuous tracking of solar irradiance, hydrogen production, weather conditions, and equipment status. \\ \hline
2 & AI-Driven Optimization & AI algorithms optimize energy production, predict maintenance needs, and enhance system efficiency. \\ \hline
3 & Predictive Maintenance & Anticipates equipment failures to reduce downtime and maintenance costs. \\ \hline
4 & Sustainability Impact & Maximizes renewable energy efficiency, reduces reliance on traditional energy, and lowers carbon footprint. \\ \hline
5 & Educational Value & Provides students and faculty with hands-on experience in advanced energy technologies. \\ \hline
6 & Operational Efficiency & Enhances system reliability and longevity by responding to real-time data and optimizing operations. \\ \hline
7 & Cost Savings & Reduces operational costs through efficient energy management and predictive maintenance. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Key aspects of the AIoT-based solar-hydrogen system in the university. is essential for understanding the performance of the solar-hydrogen system and for developing predictive models that improve system operations. Additionally, thermographic images and sensor data were used for fault detection in solar panels. This enabled the identification of issues like dust accumulation, mechanical damage, and electrical defects, which could otherwise reduce energy output. The use of machine learning algorithms further enhanced the reliability and efficiency of the solar-hydrogen system. One of this study's main objectives was to assess the Transformer model's ability to forecast energy output from the university's solar-hydrogen system. Transformer models are well-regarded for their capacity to detect complex patterns in time-series data. The model's performance was evaluated using three key metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and a second variation of MAE (MAE2). The Transformer model performed well in predicting energy production, with an MAE of 1685.04, an MSE of 4,361,833.37, and an MAE2 of 1685.038.