# 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
#### 3.1.1 Fault Detection and Diagnosis
While UAV-based methods excel in capturing high-resolution imagery, sensor-based approaches offer the advantage of real-time monitoring and fault detection. These techniques rely on electrical and environmental sensors to continuously monitor PV system performance. [PERSON] and [PERSON] [76] combined Artificial Neural Networks (ANNs) with analytical models for automated fault detection and diagnosis in PV systems. Their method forecasts power output based on irradiance and temperature, comparing predicted and actual values to identify faults, offering a compact and high-accuracy solution by integrating parameters such as open-circuit voltage and short-circuit current for fault validation. Similarly, [PERSON] et al. [77] proposed an intelligent solar panel monitoring system integrating ANNs and IoT platforms to detect shading and other PV faults. The ANN model estimates optimal power output using irradiance and temperature data, while discrepancies between predicted and actual output indicate performance issues. The IoT platform enables real-time visualization and fault alerting, enhancing energy efficiency and reducing maintenance costs. [PERSON] and [PERSON] [78] utilized the C4.5 decision tree algorithm for fault \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Type of Faults** & **AI Technique** & **Key Metrics** & **Limitations** \\ \hline \hline [PERSON] et al. [69] & Visual faults: - Glass breakage - Burn marks - Discoloration - Delamination & DNN with Random Forest classifier & Acc = 99.68\% & Limited to RGB images captured by UAV, requires extensive preprocessing \\ \hline [PERSON]- [PERSON] et al. [70] & - Spots - Cracks - Dust - Microcracks & DL with RHA and GSQA preprocessing 93\% (1000 images), 97\% (2000 images) & Acc = 87\% (500 images), 93\% (1000 images), 97\% (2000 images) & Performance depends on dataset size; preprocessing is computationally expensive \\ \hline [PERSON] et al. [71] & - Microcracks & SVM with HE preprocessing & F1 Score: 94\% & Limited to EPV dataset, may not generalize to other datasets \\ \hline [PERSON] et al. [72] & - Multiple faults - YOLOX (Modified YOLOX) & Mean Average Precision (mAP): 92.56\% & Focused on IR images, limiting applicability to RGB datasets \\ \hline [PERSON] et al. [73] & Defects posing electrical hazards & Modified YOLOv5 & mAP: 95.5\%, 1.5\% increase in precision, 2.4\% increase in recall & Improvements are incremental; model performance depends on dataset quality \\ \hline [PERSON] et al. [74] & Panel conditions - Normal - Dusty - Damaged & YOLOv5s with Gaussian and Wavelet Transform preprocessing & F1 Score: 81\% (without preprocessing), 87\% (with preprocessing) & Preprocessing adds complexity; limited F1 score improvement \\ \hline [PERSON] et al. [75] & - Normal - Duxy - Duxy - YOLOv8 with Histogram Equalization preprocessing & F1 Score: 97\% (YOLOv5) & Limited real-time testing on Raspberry Pi 4B; requires UAV technology \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of UAV-based approaches on fault detection. detection and diagnosis in grid-connected photovoltaic systems (GCPVS). Their approach processes ambient temperature, irradiation, and power ratio data to identify faults such as string faults, short circuits, and line-line faults. Over a five-day testing period, the model demonstrated high detection accuracy of 99.87% and diagnostic accuracy of 99.80%, proving its robustness for real-world applications. Similarly, [PERSON] et al. [79] introduced a k-nearest neighbors (kNN)-based fault detection technique, integrating Shewhart and exponentially weighted moving average (EWMA) monitoring schemes. The method uses residuals from simulation models combined with parametric and nonparametric thresholds for anomaly detection. Validated on a 9.54 kWp grid-connected PV system, the kNN-based approach achieved high accuracy and robustness against noise, outperforming traditional monitoring techniques and enhancing PV system reliability.