# 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
#### 3.2.2 Optimization Techniques: MPPT, Solar Tracking, and Cleaning Systems
A specialized hardware-software IoT-STS component enables real-time wireless communication between panels, ensuring optimal alignment. By integrating a backtracking strategy, the system minimizes shading effects and improves energy yield by up to 5% annually, especially in varying terrain conditions. The MPPT feature further optimizes power output, making the system highly efficient for solar energy generation. However, many focused on the development of double-axis solar tracking systems. [PERSON] et al. [171] present in their study an IoT-based two-axis solar tracking system (IoT-TASF) that integrates GPS, Artificial Neural Networks (ANNs), and image processing (IP) for precise sun tracking. The system optimizes panel orientation using real-time GPS and image data, while AI-driven decision-making adjusts for weather conditions. IoT enables remote monitoring and automation via cloud storage. Experimental results show a 59.21% power gain over fixed systems and reduced azimuth angle errors to 0.20 degrees, highlighting its efficiency and adaptability. [PERSON] et al. [172] present the design and implementation of an IoT-based dual-axis solar PV tracking system. The proposed system integrates an Arduino-based microcontroller, GPS, web-based connectivity, and Light-Dependent Resistors (LDRs) to enhance solar tracking efficiency. The LDRs detect visible light intensity, guiding the movement of PV modules for optimal alignment with the sun. The tracking controller processes four intensity signals from the LDRs to adjust the panel's position based on the sun's daily trajectory. Experimental results demonstrate that the proposed system outperforms fixed solar panel setups in efficiency and energy capture. Also, in their project, [PERSON] et al. [173] focus on the development of a two-axis solar tracking system using an Arduino Uno as the main controller. The system employs four Light-Dependent Resistors to detect sunlight intensity and two servo motors to adjust the solar panel's position for optimal light absorption. A Wi-Fi ESP8266 module facilitates data transmission to an IoT-based monitoring system, which stores and displays performance data via a web platform. Efficiency tests comparing the two-axis tracker with a single-axis system show that the proposed system generates higher power, voltage, and current, demonstrating improved energy harvesting. [PERSON] et al. [174] recently presented a dual-axis tracking system that combines two tracking approaches: one using multiple LDR sensors managed by a PID controller for sunny conditions, and another using GPS and time-based control to calculate the sun's position (azimuth and altitude) on cloudy days. An IoT network is also established to monitor and support the system remotely. This smart setup results in a 33.23% increase in energy production compared to fixed systems, while minimizing unnecessary GPS usage. The prototype features two linear actuators for dual-axis movement and a 100W solar panel. Table 9 presents a comparison of the mentioned IoT-based solar tracking and MPPT systems, detailing the tracking type, whether MPPT is implemented, IoT functionalities, performance gains, and unique contributions of each study. In the context of solar energy systems, maintaining optimal performance is paramount, and one significant challenge is the soiling of PV panels. Dust, dirt, and other particles that accumulate on PV panels can drastically reduce their efficiency and energy output. Research carried out in the United Arab Emirates [175] to assess the influence of dust on the performance of PV systems revealed that the PV power declined steadily by 1.7% per g/m\({}^{2}\) in both indoor and outdoor studies, with an accumulation of dust ranging from 5.44 g/m\({}^{2}\) over a period of 5 months; a 12.7% decrease in efficiency was detected as a result of soiling on the PV surfaces. Many projects focused on addressing this issue. In their research, [PERSON] et al. [176] show that the reduction in the peak power generated can be up to 18% in the performance of the solar PV panel.