# 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
\begin{table}
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**Study** & **Approach** & **AI Technique** & **Data Inputs** & **Fault Type** & **Key Contributions** & **Limitations** \\ \hline [PERSON] \& [PERSON] [99] & Predict power output to identify faults. & ANN & Irradiance, wind, temperature & Soiling, decorration, inverter failures & Tracks system degradation over time; ensures long-term reliability; proactive maintenance scheduling. & Requires system-specific training data; limited scalability to different PV configurations. \\ \hline [PERSON] et al. [100] & Compare predicted vs. real-time output to detect anomalies and schedule maintenance weeks in advance. & ANN + temperature, AC power output & Irradiance, temperature, AC power & Long-term degradation & Predictive alerts weeks ahead; 90\%+ anomaly detection rate; 2.3\% validation error; & Relies on specific datasets; \\ [PERSON] et al. [101] & Predict standard operational activity; send internet-based alerts for anomalies. & ANN & Environmental conditions, panel output & General anomalies & Low-cost intelligent monitoring; real-time anomaly detection; \\ [PERSON] \& P Predict target using neighboring data; flag deviations for anomalies. & CNN & Power output curves of neighboring panels & Dynamic weather-induced faults, shadowing & Addresses weather variations and shadowing; superior accuracy with unshared CNN model; uses synthetic and real-world data. \\ \hline [PERSON] et al. [103] & Dual-model architecture: SOM for generic deviations and ANN for fault classification. & SOM + ANN & SCADA data deviations, inverter faults & Generic deviations, hardware; scalable to large PV systems; reduces downtime and reliability. & Predicts faults & Needs large historical data for training; limited fault class \\ [PERSON] et al. [104] & Cluster similar patterns and predict deviations in current to detect anomalies. & K-Means + LSTM & Module output currents, irradiance, temperature & Electrical anomalies & Hybrid model achieves superior accuracy; scalable to large PV systems; reduces downtime and enhances reliability. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative table for predictive maintenance in PV systems. The reviewed predictive maintenance approaches for PV systems highlight a shift toward AI-driven fault detection and performance optimization strategies. These methods, ranging from ANN-based models for anomaly detection to advanced hybrid models incorporating deep learning and ensemble techniques, show promising results in identifying faults before failures occur. However, the reliance on specific datasets and system configurations limits their generalizability. Future research should focus on developing adaptable models capable of handling diverse environmental conditions and PV setups while integrating robust fault isolation mechanisms. The trade-off between model complexity and predictive accuracy requires consideration of data dependencies, scalability, and computational resources. Hybrid models and scalable architectures offer potential solutions for improving the applicability and efficiency of predictive maintenance in PV systems.