1. Introduction
The integration of renewable energy systems, especially solar–hydrogen systems, is becoming increasingly critical as universities seek sustainable solutions to meet their rising energy needs. Recent studies have focused on optimizing the design and implementation of these systems, with a particular emphasis on their architecture, seamless integration, and the substantial benefits they offer in promoting clean energy usage. These systems typically combine photovoltaic arrays, fuel cells, electrolyzers, and hydrogen storage, integrated with energy management systems tailored to the university’s needs. Research has shown that integrating renewable energy sources with existing grid networks can significantly enhance sustainability and reliability, reducing CO2 emissions and grid dependency while providing cost savings. The optimization of these integrated systems is crucial for meeting varying energy demands, especially in rural areas. While challenges in implementation exist, the potential for sustainable energy harvesting in academic environments is promising, with benefits including increased renewable fraction, reduced emissions, and improved energy management. Solar–hydrogen systems are emerging as promising sustainable energy solutions for universities. These systems integrate solar photovoltaic technology with water electrolysis to produce hydrogen for energy storage and use. The design and implementation of such systems in university settings involve considerations of energy management, infrastructure integration, and economic viability [
1,
2,
3]. While the technology shows potential for reducing campus energy consumption and achieving sustainability goals, challenges remain in terms of scalability and cost-effectiveness. According to economic calculations, the components that absorb light have a greater influence on the cost of producing hydrogen than the materials used in electrolysis. Optimized designs have the ability to produce hydrogen for less than USD 2.90 per kilogram after distribution and compression. In order to solve implementation issues and investigate the long-term effects of solar–hydrogen systems on the economy and education in university settings, more study is required [
4,
5].
Solar energy is used in solar–hydrogen systems to create hydrogen, a clean, storable energy source. Photovoltaic (PV) cells are commonly utilized in these systems to provide electricity, which is subsequently utilized in electrolyzers to separate water into hydrogen and oxygen [
6]. By allowing for long-term energy storage, this strategy tackles the unpredictability of renewable energy sources. These integrated solar PV–hydrogen systems can meet diverse energy needs, powering homes and vehicles while emitting only water vapor [
7,
8]. Various methods for solar hydrogen production exist, including photochemical, semiconductor, photobiological, and hybrid systems. Research focuses on developing more efficient electrolyzers and advanced storage media to enhance overall system effectiveness. Solar–hydrogen technology offers potential for preserving major energy system options and supporting the transition from fossil fuels to sustainable energy sources [
9].
Fault detection in solar panels is crucial for maintaining system efficiency and longevity. Recent research has focused on developing intelligent and automated methods for identifying faults in photovoltaic (PV) systems. IoT-based approaches using smart monitoring devices can detect faults in real-time, improving overall system performance [
10]. Advanced techniques such as Bayesian belief networks [
11] and fuzzy inference systems combined with multi-resolution signal decomposition [
12] have been proposed for more accurate fault detection and diagnostics. These methods can identify various types of faults, including DC-side short-circuits, which are particularly challenging to detect under low-irradiance conditions. Developing efficient fault detection and diagnostic procedures requires a thorough understanding of physical, electrical, and environmental defects in PV systems [
13]. Implementing reliable fault detection methods is critical for optimizing energy production, preventing damage to PV panels, and mitigating potential fire hazards. IoT-based approaches using wireless sensor nodes and machine learning algorithms enable real-time fault detection and diagnosis, reducing downtime and maintenance costs. A comprehensive understanding of physical, electrical, and environmental faults in PV systems is crucial for developing effective fault detection techniques. Various methods have been suggested for PV fault diagnostics, focusing on the DC side of the system. Implementing reliable fault detection is essential for optimizing energy production, preventing damage to PV panels, mitigating fire hazards, and extending the operational lifespan of solar energy systems [
14,
15].
The purpose of this research is to analyze the energy potential of solar–hydrogen systems within university settings, specifically at the Kangwon National University’s Samcheok campus, using advanced machine learning models. Additionally, this study aims to design an efficient fault detection system for solar panels by comparing the performance of Convolutional Neural Networks (CNNs) and ResNet-50. Through this case study, the research seeks to optimize renewable energy systems in academic environments, enhancing their reliability, sustainability, and economic viability.
3. Research Methodology
3.1. Data Collection
In order to evaluate energy potential and find solar panel defects, gathering data is an essential part of this study process. The energy data are sourced from Kangwon National University’s Samcheok campus, with a particular focus on the Green Energy Building as the sample site. These data encompass metrics related to solar energy production, including historical weather patterns, solar irradiance levels, temperature, and humidity. These variables are vital for predicting energy output using advanced machine learning models such as the Transformer model. Historical weather data offer insights into seasonal trends while solar irradiance data reflect the available sunlight, both essential for accurate energy forecasting.
For solar panel fault detection, the study collects data in the form of images and sensor readings. This includes thermographic images that reveal temperature variations on the solar panel surfaces, which can indicate potential issues like hotspots or degradation. Furthermore, sensor data—such as voltage and current measurements—assist in locating abnormalities in performance. Convolutional Neural Networks (CNNs) and ResNet-50 are two machine learning models that are trained with these photos and sensor data in order to automatically identify and categorize solar panel defects. The university’s solar–hydrogen system analysis and problem detection procedures depend critically on the integration of these datasets.
3.2. Energy Potential Analysis Using Transformer Model
This section provides a detailed explanation of the Transformer model architecture implemented in Python, used to predict energy output in solar–hydrogen systems. The Transformer model was first created for natural language processing, but because of its capacity to identify long-term dependencies in data, it has grown in popularity for time series forecasting. Because of the self-attention mechanisms built into its architecture, the model is able to assess the importance of different time steps in the input data, which makes it a powerful tool for studying complex, multivariate datasets, such as those found in the energy production industry.
A dataset of historical energy production data, meteorological data, and solar irradiance levels gathered from the university’s solar energy installations is used to train the Transformer model. Subsets of the dataset are used for testing, validation, and training to make sure the model learns well and can be applied to fresh, untested data. To maximize model performance, important training parameters including learning rate, batch size, and number of epochs are carefully adjusted. Using patterns from past data, the model is trained to predict future energy output. Overfitting is avoided by using validation procedures to monitor the model’s development.
Several metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Error Squared (MAE2), are used to assess the Transformer model’s performance. An easy-to-understand indicator of prediction accuracy, MAE yields an average of the absolute disparities between values that were anticipated and those that were observed. By accounting for the square of the differences, MSE helps to discover notable outliers by assigning greater weight to larger errors. The MAE2 variance, which squares the MAE, highlights bigger disparities even more, enabling a more thorough assessment of the model’s forecasting performance. When combined, these measures offer a thorough evaluation of how well the Transformer model predicts energy output in a solar–hydrogen system.
3.3. Solar Panel Fault Detection Approach
This section outlines the methodology for solar panel fault detection, with a focus on the architectures of ResNet-50 and Convolutional Neural Networks (CNNs), along with the metrics used for performance evaluation and training, implemented in Python.
CNNs are frequently utilized in image processing and are very good at locating solar panel defects. Convolutional layers, one of the several layers that make up CNN architecture, are capable of automatically identifying elements in images, such as edges, textures, and patterns that point to defects like fractures or hotspots. The network’s efficiency is increased by further processing these features through pooling layers, which lower the dimensionality of the data. The retrieved features are then interpreted by the fully connected layers at the end of the network to determine whether a problem is present in the solar panel photos.
By adding residual connections, ResNet-50, a more sophisticated deep learning model, expands upon the fundamental CNN architecture. The network can go deeper thanks to these connections without running into the vanishing gradient issue. ResNet-50, with its 50 layers, can extract more complicated characteristics and improve the accuracy of complex defect detection. Compared to a standard CNN, the residual connections allow the model to learn more abstract and detailed features while maintaining its efficiency as it goes deeper.
Important measures like data augmentation are done to the training dataset to increase the variety of input images and boost the models’ capacity to generalize to new data in the training process for both models. Color corrections, flips, and rotations of images may be part of this procedure. The models are trained across a number of epochs using optimization methods to minimize the loss function, such as the Adam optimizer or stochastic gradient descent. To prevent overfitting and guarantee that the models can correctly identify errors in fresh, unviewed photos, the training process is closely watched.
Several measures are utilized to assess and compare CNN’s and ResNet-50’s performance. Precision shows if the flaws that have been recognized are accurate, whereas accuracy evaluates the percentage of faults that have been identified correctly. The model’s recall evaluates its capacity to find all potential errors, and the F1-score offers a trade-off between recall and precision. Since faster models are preferred for real-time monitoring, inference time—the amount of time needed for the model to analyze an image and create a prediction—is also taken into account. When combined, these measures offer a comprehensive assessment of the two architectures’ efficacy and efficiency in solar panel fault detection.
4. Results and Discussion
4.1. Energy Potential Analysis
The three main metrics used in this section to assess the Transformer model’s prediction of the university’s solar hydrogen energy potential are Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Error 2 (MAE2). Forecasts of energy production that are accurate are necessary to maximize energy storage, resource management, and the overall effectiveness of renewable energy systems. The university’s solar hydrogen output was predicted using the Transformer model, a potent machine learning method renowned for its capacity to spot intricate patterns in time series data.
The average difference between the model’s predictions and the actual energy output, as indicated by the MAE result (
Figure 1) of 1685.04, is around 1685 units. The average prediction error (MAPE), which may be simply interpreted, is a simple metric that gives information about the overall accuracy of the model.
The average of the squared discrepancies between the expected and actual values is represented by the MSE result (
Figure 2), which is 4,361,833.37. Larger errors are given more weight in this metric, since it squares the errors before averaging. Even if the majority of predictions are quite near to the actual values, there are certain cases with sizable mistakes, which raise the overall MSE, as indicated by the relatively high MSE.
The MAE2 result (
Figure 3) of 1685.038, which is a variation of the MAE, underscores the importance of minimizing errors. This result, closely aligning with the original MAE, indicates a consistent average deviation of around 1685 units, even when the error is squared.
These metrics form a comprehensive evaluation of the Transformer model’s accuracy. The relatively low MAE and MAE2 suggest the model is generally effective, while the higher MSE points to areas where larger errors might need to be addressed, especially when extreme values are involved. This analysis feeds directly into the broader narrative of optimizing renewable energy systems by improving prediction accuracy, leading to more efficient energy management. The detailed evaluation using these metrics directly informs both the Model Performance and Error Analysis sections. By understanding how the Transformer model performs in predicting solar hydrogen energy production, we can identify its strengths and weaknesses, ultimately guiding future improvements and refinements to enhance its real-world application.
The Transformer model’s ability to forecast the university’s solar hydrogen energy potential is evaluated visually in the graph in
Figure 4 (Model Performance). The model’s predictions (represented by the orange line) are compared against the actual measured values (blue line) over a substantial period, revealing key insights into its efficacy. Overall, the model demonstrates a commendable ability to track the fluctuations and trends in solar hydrogen production. The close alignment of the predicted and real values in many instances suggests that the Transformer model is effective at learning and generalizing the patterns of energy generation, which are influenced by factors such as sunlight availability, seasonal variations, and perhaps the operational efficiency of the solar–hydrogen systems. This alignment implies that the model can serve as a reliable tool for forecasting the university’s energy potential, aiding in the strategic planning of energy use and storage.
Despite the Transformer model’s generally strong performance, the graph also uncovers several areas where its predictions fall short, highlighting some limitations and sources of error (Error Analysis). Notably, there are significant discrepancies between the predicted and actual values during certain periods, particularly around the 1800 to 2100 mark on the X-axis. These deviations suggest that the model occasionally struggles with accurately predicting the solar hydrogen output, leading to instances of both overprediction and underprediction. These errors might stem from the model’s inability to fully account for sudden or extreme changes in environmental conditions, such as unexpected weather events that alter solar irradiance. Additionally, the model may have been trained on data that did not sufficiently capture the full range of possible conditions, leading to less accurate predictions in outlier scenarios. These issues highlight the importance of ongoing model refinement.
To mitigate these errors and improve the model’s predictive accuracy, several approaches could be considered. First, incorporating more comprehensive environmental data, such as real-time weather information or atmospheric conditions, might help the model better anticipate fluctuations in solar energy production. Secondly, enhancing the model’s architecture or employing techniques like ensemble learning could improve its robustness and ability to generalize from diverse datasets. Lastly, a thorough analysis of the periods of significant deviation could provide insights into the specific factors that the model currently overlooks, guiding targeted improvements. By addressing these limitations, the Transformer model could be further optimized to provide more reliable and accurate forecasts of the university’s solar hydrogen energy potential.
4.2. 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.
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.
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. The high loss value of 78%, on the other hand, indicates a significant difference between the actual and predicted labels, indicating that the model may not be completely accurate in capturing the intricate patterns present in the data. In contrast, the ResNet-50 model demonstrated superior performance with an accuracy of 85% and a significantly lower loss value of 42%. This improvement suggests that ResNet-50’s deeper architecture and advanced feature extraction capabilities allow it to more effectively recognize intricate patterns and features in the solar panel images, resulting in more accurate predictions and a lower prediction error. The reduced loss in the ResNet-50 model indicates that it is better at minimizing errors in its predictions, making it more reliable for fault detection in solar panels. However, even with this enhanced performance, the presence of a 42% loss value reveals that there is still room for further refinement to enhance accuracy and reduce errors. This could involve fine-tuning the model parameters, improving the quality of the training data, or integrating additional data preprocessing steps. Overall, the analysis underscores the potential of ResNet-50 as a more effective model for solar panel defect detection compared to VGG-16, particularly in terms of accuracy and loss. Nonetheless, both models exhibit promising capabilities, and with further optimization, they could provide even more reliable tools for maintaining and monitoring the efficiency of solar energy systems.
5. Future Prospects
5.1. Analysis and Future Directions
This study assesses how well machine learning models perform in two crucial domains: energy potential analysis, using the Transformer model, and solar panel failure detection. There are significant distinctions between the ResNet-50 and VGG-16 architectures when defect detection models are analyzed. With a loss value of 78%, the VGG-16 model had a comparatively high accuracy of 80%. This suggests that although the model was able to accurately detect most errors, it had difficulty with more intricate or subtle problems, which led to a substantial discrepancy between expected and actual results. Conversely, the ResNet-50 model outperformed the others, with an accuracy of 85% and a significantly lower loss value of 42%. ResNet-50’s deeper design, which enables it to extract more complex characteristics and more accurately recognize patterns in solar panel photos, is responsible for this improvement. ResNet-50 appears to be a more dependable model for identifying solar panel defects, as seen by its lower loss value, which also shows that it is more successful at reducing prediction errors. Even with these improvements, there is still opportunity for additional optimization to raise the model’s precision and lower its prediction error.
Three important metrics were utilized to assess the Transformer model’s performance in the context of energy potential analysis: Mean Absolute Error (MAE), Mean Squared Error (MSE), and a variation known as MAE2. The Transformer model was used to estimate the university’s solar hydrogen energy output. An accurate indicator of the model’s accuracy is the MAE result of 1685.04, which shows that the model’s predictions and the actual energy production differ by an average of 1685 units. The MSE result of 4,361,833.37 suggests that while the majority of predictions are close to the actual values, there are some significant outliers that increase the overall error. This is because MSE places more weight on larger errors by squaring them. The MAE2 result of 1685.038, which is very close to the original MAE, shows a consistent average error, further validating the model’s performance. Despite these generally strong results, the Transformer model does show some limitations, particularly in scenarios where the energy output varies significantly from the norm, suggesting that further refinement is necessary.
Moving forward, several strategies could be implemented to improve both the fault detection models and the energy potential prediction model. For fault detection, incorporating more diverse training data, such as additional environmental factors, could help models like ResNet-50 further improve their accuracy. Additionally, hybrid approaches that combine different architectures might leverage the strengths of each to better handle complex or subtle faults. For the Transformer model used in energy potential analysis, integrating more comprehensive environmental data, such as real-time weather conditions, could help the model anticipate and account for sudden changes in solar energy output. Further, exploring advanced model architectures or ensemble techniques could enhance the model’s ability to generalize from the data and reduce significant prediction errors. In both areas, ongoing model refinement, real-time monitoring, and continuous updates to training data will be crucial for maintaining the accuracy and reliability of these machine-learning applications. By addressing these areas, the models can be better optimized to support the efficient and sustainable operation of the solar–hydrogen energy systems at the university and potentially in broader applications.
5.2. AIoT-Based Solar–Hydrogen System in the University
Energy management and sustainability can be significantly improved by integrating artificial intelligence (AI) and Internet of Things (IoT) technology into a university’s solar–hydrogen system (
Table 6). Using IoT sensors and AI-driven analytics, this AIoT-based system improves predictive maintenance, real-time monitoring, and overall system efficiency. Critical parameters including solar irradiance, hydrogen generation, weather, and equipment performance are continuously monitored via IoT sensors. Artificial intelligence algorithms analyze the gathered data to forecast maintenance requirements, enhance operational effectiveness, and maximize energy generation and storage. For example, AI can automatically modify operations to maintain constant hydrogen production levels if the system senses a drop in solar irradiation.
The university gains a number of advantages from this combination. Quick responses to changes in system performance are made possible by real-time monitoring and control, and AI-driven predictive analytics helps to detect equipment failures, which lowers maintenance costs and downtime. Furthermore, by maximizing the efficiency of renewable energy sources and lowering the carbon imprint, the system helps the university achieve its sustainability goals. The AIoT-based solar–hydrogen system also functions as a research and teaching tool, giving academics and students hands-on exposure with cutting-edge energy technologies. In addition to enhancing energy management, this arrangement supports the university’s overarching goals of advancing sustainability and cutting-edge energy solutions.
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 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. 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.