# 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
This architecture effectively shows how IoT tools can be integrated into PV monitoring systems to improve data accessibility, operational transparency, and system scalability in modern smart energy environments. On the other hand, [PERSON] and [PERSON] [139] describe solar monitoring systems that utilize a microcontroller and sensors to collect data, which are then transmitted to clients via Amazon Web Services (AWS). The incorporation of IoT in these systems enhances the efficiency of solar energy usage. In paper [140], [PERSON] et al. introduce a low-cost wireless solution using LoRa technology for monitoring PV power plants. The system offers long-range communication with minimal power usage, integrating open-source sensors and LPWAN for efficient IoT-based data exchange. It highlights the system's application in real PV installations and assesses its performance in grid-connected environments. In their study, [PERSON] (2023) [141] introduces a real-time data acquisition and monitoring system for PV panels using IoT technology, specifically employing a Siemens S7-1200 PLC. The system collects data from multiple sensors, including voltage, current, temperature, and irradiance, and sends this information to a cloud platform (Ubidots) for remote monitoring. Three visualization methods were utilized: SIMATIC WinCC for local monitoring, Excel for data logging, and Ubidots for online access. Tested in Kirkuk, Iraq, the system demonstrated high accuracy and reliability over a 10 h period, showing its potential for scalable, low-cost PV monitoring in remote locations. Similarly, [PERSON] [142] developed an IoT-based system to enhance the efficiency and functionality of solar array farms by enabling real-time monitoring of solar energy. Many countries have contributed to this field; for example, Japan developed a first-generation smart monitoring device (SMD) equipped with IoT-enabled panels. The proposed model uses sensors and PV arrays, relying on advanced vision and fusion algorithms. This innovative approach introduces a new generation of solar array farms, incorporating mobile analytics to identify faults, benefit users, and perform continuous optimization, thereby improving operational quality. It also aids in power prediction and provides robust monitoring capabilities. Several works incorporate predictive models and fault detection to enhance solar panel efficiency. [PERSON] et al. [143] propose an IoT-based control and monitoring solution for a grid-connected hybrid solar power system. It automatically detects and prevents PV faults to avoid damage and maintain system performance. Fault detection is handled by an Arduino-based system using voltage, current, and temperature sensors, and a fuzzy non-linear autoregressive exogenous model classifies faults. The system ensures reliable power delivery and grid stability, validated through real-time implementation and simulations. Intheir study, [PERSON] et al. [144] present a low-cost IoT-based embedded solar PV monitoring system that leverages a GPRS module and a microcontroller to transmit real-time data to the internet. The system enables global access to production metrics, facilitating maintenance, fault detection, and comprehensive data logging at regular intervals. Similarly, [PERSON] [145] introduces a cost-effective IoT-based methodology for remote monitoring of PV plants, enabling real-time performance evaluation with Wi-Fi, preventive maintenance, fault detection, and historical data analysis. The approach leverages web-based interfaces to automate plant monitoring and ensure operational efficiency even in challenging locations. [PERSON] et al. [146] propose an Intelligent Monitoring System (IMS) for PV systems using IoT technology. The system enables real-time monitoring, fault detection, and power prediction through a personal cloud server and a web-based interface. It is scalable, cost-effective, and designed for easy deployment, ensuring continuous monitoring and performance optimization of PV plants. Additionally, [PERSON] et al.