# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Applications of AI in PV Systems
#### 3.1.2 Predictive Maintenance  
Predictive maintenance is a proactive approach that leverages advanced techniques to anticipate equipment failures before they occur, ensuring timely interventions to maintain system efficiency and reliability. In the context of PV systems, predictive maintenance plays a critical role in minimizing system downtime, reducing operational costs, and improving energy output. By analyzing environmental and performance data, AI-based methods, such as ANNs and other statistical models, are capable of forecasting maintenance needs, identifying degradation trends, and detecting anomalies that might lead to potential faults. These approaches represent a significant advancement over traditional reactive or preventive maintenance strategies. Several studies have demonstrated the application of predictive maintenance techniques in PV systems, showcasing their potential to improve system reliability and optimize maintenance planning. [PERSON] and [PERSON] [99] developed a Prognostics and Health Management (PHM) system leveraging ANNs to predict power output based on environmental inputs such \begin{table}
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**Study** & **Approach** & **AI Technique** & **Input Data** & **Fault Type** & **Key Metrics** & **Limitations** \\ \hline [PERSON] et al. [95] & Fault anomaly detection & VAE & DC voltage, DC current, wind speed, precipitation intensity, cloud cover, and UV index & Rare faults, unknown faults & 92.06\% Fault detection rate & High false positives in some cases \\ \hline [PERSON] et al. [96] & Fault diagnosis & RF + Feature transformation & RF + Feature current signals & Three-phase AC current signals & Open-circuit faults in LGBT switches & High accuracy under varying load conditions & Dependent on sensor accuracy and transformation technique \\ \hline [PERSON] et al. [97] & Fault detection & LSTM & Three-phase current signals & HIFs & Accuracy: 91.21\% & Computational cost of LSTM; simulation-based validation \\ \hline [PERSON] et al. [98] & Fault diagnosis & ANN & Measured fault patterns & Open-switch faults & Diagnosis time: 0.5 ms, Accuracy: nearly 100\% & May not generalize well to unknown converter topologies \\ \hline \hline \end{tabular}
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Table 5: _Cont._as irradiance, wind, and temperature. The PHM system monitors system health, identifying faults like soiling, degradation, and inverter failures by detecting performance deviations. This approach allows proactive maintenance scheduling and tracks system degradation over time, ensuring long-term reliability and preventing catastrophic failures. [PERSON] et al. [100] proposed an advanced anomaly detection approach combining ANNs and Triangular Moving Average (TMA) analysis to identify long-term degradation trends. Their model compares predicted AC power output, derived from solar irradiance and temperature data, with real-time measurements to detect deviations, issuing alerts that enable maintenance to be scheduled weeks in advance. This method achieved over 90% accuracy in anomaly detection and demonstrated significant potential for reducing power losses and preventing failures. Additionally, [PERSON] et al. [101] introduced a low-cost intelligent monitoring system that employs a compact ANN to predict the standard operational activity of PV panels based on environmental conditions. By comparing predicted performance with actual output, the system detects anomalies that indicate the need for maintenance and automatically notifies administrators or maintenance teams via internet-based alerts. However, the system lacks functionality for isolating or removing malfunctioning panels. Extending these efforts, [PERSON] and [PERSON] [102] applied CNNs to predictive maintenance by analyzing power output curves of panels. Their method predicted the daily power curve of a target panel using neighboring panels' power curves, flagging large deviations as indicators of malfunction.