# 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
Dust accumulation is a common issue impacting PV efficiency. [PERSON] et al. [80] addressed the impact of dust on PV systems by developing an ML-based dust detection method and an automated cleaning system. The study implemented various ML classifiers, with the ANN model achieving the highest accuracy of 98.11%. Upon detecting dust, the system activates a water sprinkler to clean the panels, restoring efficiency to 14.87%. In a related effort, [PERSON] et al. [81] proposed an Artificial Neural Network-Based Model (ANNBM) to estimate power loss due to partial shading. The system uses a Multilayer Perceptron (MLP) to detect deviations between predicted and actual outputs, providing a computationally efficient alternative to complex mathematical models. [PERSON] et al. [82] designed a three-layer ANN model targeting short-circuit fault localization in PV modules. While highly effective in identifying faulty modules, the system requires extensive data training for scalability across large PV installations. In the realm of arc fault detection, [PERSON] et al. [83] introduced a novel DL-based methodology, DA-DCGAN (Domain Adaptation and Deep Convolutional Generative Adversarial Network). This technique generates synthetic arcing data from normal PV loop current data using a DCGAN and applies domain adaptation to train a lightweight CNN classifier for accurate cross-domain fault diagnosis. The approach was validated through real-time experiments on a 1.5 kW grid-connected PV system and pre-recorded data, achieving high detection accuracy. The DA-DCGAN eliminates reliance on real fault datasets, making it highly practical for real-world applications where labeled fault data are scarce. Additional research has focused on improving fault classification accuracy. [PERSON] et al. [84] developed a modified neural network model for fault diagnosis, integrating extension theory to improve learning efficiency and reduce memory consumption. The model achieved high accuracy in identifying 10 types of PV faults, outperforming traditional MLP networks. Similarly, [PERSON] and [PERSON] [85] employed a Probabilistic Neural Network (PNN) for detecting, classifying, and locating faults in PV arrays. The method leverages environmental and electrical data to enhance fault detection accuracy. Based on manufacturer datasheet information, a novel PV system modeling technique was introduced to validate the approach. Building on the idea of Decision Tree algorithms, [PERSON] et al. [86] developed a Decision Tree (DT) model for fault detection and classification trained on 764,529 PV instances under 28 fault conditions, achieving high accuracy in both fault detection (up to 99.98%) and classification (up to 99.8%) and integrating into a microcontroller-based system for real-time monitoring. In parallel, [PERSON] et al. [87] implemented a Random Forest (RF)-based fault detection model, analyzing array voltage and string currents without requiring environmental condition inputs such as irradiance or temperature to identify various PV faults, including line-line faults, degradation, open circuits, and partial shading. Their RF system, tested in MATLAB and real PV setups, achieved 99.994% detection accuracy and 99.952% classification accuracy but depended on large labeled datasets for training. [PERSON] and [PERSON] [88] employed a kNN-based fault detection and classification technique,focusing on real-time monitoring of faults like open circuit, line-line faults, and partial shading scenarios. The method, validated on experimental I-V characteristic data under varying irradiance and temperature conditions, achieved a high classification accuracy of 98.70%. Also, [PERSON] et al. [89] proposed a PV array fault diagnosis method combining Fuzzy C-Mean (FCM) clustering and fuzzy membership algorithms to improve accuracy and robustness in fault classification. The method clusters fault sample data into six fault types, such as short circuits and shading, using FCM and calculates membership degrees for fault diagnosis based on fuzzy normal distribution.