# 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
Several studies have focused on IoT-based monitoring systems that track both solar power generation and environmental conditions to enhance performance and ensure efficient operation. [PERSON] and [PERSON] [120] aimed to develop a smart IoT solar system capable of capturing the optimal amount of solar energy and generating power based on meteorological data, while also predicting various other factors. They employed the Hidden Markov Model (HMM) for prediction, considering the probabilistic correlation between past and future values in time series data. The results showed that by deploying a single panel dead state, the system was able to accurately predict solar energy generation based on time series data, closely reproducing actual power output. Similarly, [PERSON] et al. [156] propose a remote and real-time monitoring system for solar PV systems using IoT to address performance variability due to environmental conditions. This IoT-based system is structured into three main components: data acquisition, a data gateway, and a smartphone application display. Data acquisition is highly accurate, achieving 98.49% accuracy in capturing data, which is then transmitted through the data gateway. The gateway effectively relays the data to the smartphone app, allowing users to view graphical representations with a mean transmission time of 52.34 s. [PERSON] et al. [157] present a cost-effective, open-source IoT solution for real-time monitoring of solar power generation and environmental conditions. Designed as a laboratory prototype, the system can be easily extended for large-scale PV stations with minimal adjustments. It also includes an alert feature that notifies remote users when solar power generation quality deviates from predefined standard values. Various studies have explored dust monitoring systems for PV installations, emphasizing their role in detecting dust accumulation and ensuring optimal performance through timely maintenance. In their study, [PERSON] et al. [158] focus on reducing energy losses in PV systems caused by dust deposition. The proposed solution leverages an IoT-based framework with a modular design. Each module uses an embedded controller to monitor the status of individual PV panels, and the data are sent to a cloud system for real-time analysis. An automatic maintenance decision algorithm processes the open-circuit voltage values of the panels to determine when maintenance is necessary. This system helps ensure optimal performance throughout the PV system's lifecycle by enabling timely maintenance actions. The prototype successfully met its objectives. [PERSON] et al. [159] propose an IoT-based system that was developed to monitor dust accumulation on solar panels and automatically trigger a cleaning system when necessary. The system uses a dust sensor to detect and monitor the amount of dust on the panels. Once the accumulated dust reaches a certain threshold, the cleaning mechanism is activated. Additionally, ambient temperature and humidity are monitored using sensors, and the data are transmitted via the Blynk IoT platform. In the study by [PERSON] et al. [160], a cloud-based Solar Conversion Recovery System (SCRS) was developed, integrating IoT technology for remote monitoring of PV panel soiling. The system used low-cost sensors and an Artificial Neural Network to optimize scalability and reduce hardware requirements. Multiple ANN models were tested, with the most effective configuration achieving a mean squared error of 0.0117 and an R\({}^{2}\) of 0.905. Data from soiling stations were transmitted wirelessly using the MQTT protocol,eliminating the need for extra wiring. The system's average error rate in estimating the soiling ratio was 4.33%. Furthermore, the limited lifespan of rechargeable batteries poses a challenge for IoT devices and their applications, as they cannot ensure a constant power supply over time. To address this issue, [PERSON] et al. [161] propose a solution focused on energy harvesting through an interconnected network of devices drawing power from their surroundings.