# 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
These intelligent systems optimize cleaning schedules, conserve resources, and minimize human intervention by leveraging weather data, dirt accumulation metrics, and predictive maintenance algorithms. This combination ensures improved efficiency, reduced operational costs, and sustained solar panel performance. [PERSON] and [PERSON] [181] developed a cleaning system with small holes positioned above the PV cells to spray water over them. This design increased the power output of the cells by 17% and improved PV cell efficiency to 3.26%. Although it is a very cost-effective, and efficient method for farmers even in hot weather, it is not suitable for arid regions. Moreover, manual labor is needed to remove sticky particles and for the maintenance of the pump. Also, a cost-effective water spray-based cleaning system was constructed to address the soiling effect on PV modules in the study [182] of [PERSON] et al. The system successfully restored 98% of the PV module efficiency and was considered suitable for commercial-scale applications. However, concerns were raised about the significant drop in efficiency when cleaning intervals were extended. A cleaning robot that uses a water sprayer and roller brush to remove dust from the panel surface was proposed by [PERSON] et al. [183]. An efficiency increase of 30-33% was observed; however, the need for external power sources adds to the system's overall cost. Meanwhile, the study by [PERSON] et al. [184] proposed a robotic system using a silicone rubber foam brush. Although it was found that a solar power plant cleaned every two weeks had an average performance loss of approximately 1.5% compared to daily cleaning with robotic technology, the system's effectiveness in removing sticky dirt, such as bird droppings, remains uncertain. [PERSON] et al. [185] investigated the use of Surface Acoustic Wave (SAW) technology to clean solar PV panel surfaces. It achieved a 62% voltage rise, aiming to avoid the damage associated with abrasive mechanical cleaning methods. However, the technology proved less effective at removing dust particles smaller than 0.2 mm. On the other hand, [PERSON] et al. [186] proposed a compressed air jet spraying system, capable of enhancing efficiency by up to 15%. Additionally, [PERSON] [187] developed a robotic cleaning system equipped with a waterproof pneumatic suction pump specifically for panel cleaning. However, its drawback lies in its inability to eliminate sticky dust. [PERSON], as well [188], proposed deploying autonomous vehicles for cleaning solar panels. However, it is expensive and does not have sufficient flexibility for quick movement. Table 10 presents a comparison of the mentioned cleaning methods, detailing the approach used, efficiency improvements achieved, and associated limitations. MPPT techniques and solar tracking systems significantly enhance solar energy output by optimizing power extraction and panel orientation under varying environmental conditions. However, their effectiveness is contingent on precise algorithms, which can be computationally intensive and sensitive to hardware failures. Advanced MPPT methods, such as neural networks, are costly and require specialized expertise, making them less accessible to small-scale applications. Similarly, while solar tracking systems improve energy capture by aligning panels with the sun, their mechanical complexity raises installation costs and maintenance challenges, particularly in harsh climates. Despite these advantages, both technologies require careful cost-benefit analysis and continuous innovation to remain economically viable. On the other hand, IoT-based cleaning systems offer significant benefits in optimizing solar energy systems: they allow for predictive maintenance and improving overall efficiency. However, their reliance on reliable connectivity makes them less effective in remote or poorly connected areas, limiting their applicability.