# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Applications of IoT in PV Systems
#### 3.2.1 Solar Monitoring Systems
[147] propose in their paper an algorithm that uses IoT and data-mining techniques to monitor power generation and detect faults in solar power systems. The algorithm helps identify areas of failure or defects, enabling quick corrective actions to improve the efficiency of large-scale solar power stations. [PERSON] et al. [148] researched an IoT-based system for monitoring power generation, efficiency, and other key parameters in solar panels to minimize breakdowns and faults, thereby enhancing efficiency. Effective monitoring and maintenance of PV solar panels are crucial for preserving system integrity and sustaining optimal efficiency. [PERSON] et al. [149] propose an IoT platform for PV maintenance, utilizing classification algorithms to detect performance ratio patterns. A case study using SCADA data from a Spanish solar plant applies Shapelets and k-nearest neighbors (KNN), with KNN preferred for its faster execution. The platform improves PV maintenance by enabling timely intervention through performance monitoring. Also, [PERSON] et al. [150] present an IoT-based system for fault detection and analysis in solar PV systems. The system effectively identifies, classifies, and analyzes faults in real time, ensuring 98.95% accuracy. Real-time data collection at both fog and cloud levels enhances fault detection reliability, achieving 100% data communication integrity and 98% availability. [PERSON] et al. [151] developed a prototype device for fault detection in stand-alone solar power systems using an internet-based approach. The system tracks real-time data on electrical output, air temperature, and sunlight levels via a web application. It identifies faults such as open circuits, shading, and dust accumulation on solar panels, providing user notifications. The prototype is cost-effective, easy to install, and requires no additional components or complex setup. [PERSON] et al. [152] present an IoT-based PV monitoring system focused on fault detection and maintenance. Using the TMS320F28335 DSP and ZigBee communication, the system collects and transmits data to a gateway, which also captures geographic and visual information. A PV monitoring website displays real-time data and employs an Extreme Learning Machine (ELM) for fault diagnosis, achieving 97.5% accuracy. Users receive fault alerts via email, ensuring timely maintenance and improved system reliability. As well, [PERSON] et al. [153] propose an IoT-based real-time monitoring system for PV fault detection and maintenance. Utilizing ZigBee for node communication and 4G for cloud transmission, the system processes operational data using a Probabilistic Neural Network for fault diagnosis. It enables remote monitoring via web and mobile interfaces, diagnosing faults in DC output, AC output, and power generation. By leveraging cloud diagnostics, the system reduces local device costs while ensuring efficient fault detection and maintenance. [PERSON] et al. [154] developed an IoT-based system for real-time fault detection and automated control of solar panels. Equipped with sensors and block management modules, the system continuously monitors electrical data, identifies faults, and sends maintenance alerts. Additionally, it adjusts panel orientation based on sunlight direction, minimizing human intervention and enhancing operational efficiency. In their study, [PERSON] et al. [155] present a smart IoT-based remote sensing prototype for fault detection and identification in PV arrays. It uses voltage, current, and temperature sensors to incorporate a system with an automated fault detection algorithm capable of identifying issues such as open circuits, short circuits, dust accumulation, and shedding effects. Tested at the Renewable Energy Laboratory of Jijel University, Algeria, the prototype demonstrated effective remote monitoring and fault identification. Users can check the system status online via a website, with IoT-enabled notifications also sent via SMS.