# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Bridging AI and IoT: Innovations in PV Energy Management  
The integration of AI and the IoT, collectively known as AIoT, is revolutionizing the way we manage and optimize PV systems. While IoT provides the necessary infrastructure to collect real-time data from solar panels and related components, AI enables the analysis and processing of these data to make intelligent decisions. Together, AI and IoT enable more efficient and automated energy management in PV systems. AIoT applications can optimize performance by predicting maintenance needs, improving energy storage, and enhancing system efficiency through real-time data analysis. This synergy not only maximizes energy production but also helps reduce operational costs, making AIoT a key enabler of smarter, more sustainable PV systems. In this section, we will examine some key contributions that bridge AI and IoT, emphasizing their synergistic role in optimizing PV system performance in real-world scenarios. Starting with smart energy management systems, where [PERSON] et al. [189] have developed a smart solar microgrid system that uses Arduino and IoT devices to collect real-time environmental data, like sunlight and temperature. The system is controlled by a Multi \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Method** & **Efficiency Gain** & **Limitations** \\ \hline [PERSON] and & & 17\% increase, 3.26\% & Not suitable for arid \\ Ameri [181] & Water spray system & efficiency gain & regions, manual \\  & & & maintenance required \\ \hline [PERSON] et al. [182] & Water spray system & 98\% efficiency restoration & Efficiency drops with \\  & & & extended cleaning intervals \\ \hline [PERSON] et al. [183] & Water sprayer + roller & & \\  & brush robot & 30–33\% increase & Requires external power, \\  & & & higher cost \\ \hline [PERSON] et al. [184] & Silicone rubber foam brush & 1.5\% loss with bi-weekly cleaning & Effectiveness on sticky \\  & robot & & cleaning & dirt uncertain \\ \hline [PERSON] et al. [185] & Surface Acoustic Wave (SAW) & 62\% voltage rise & Ineffective for particles \\  & & & smaller than 0.2 mm \\ \hline [PERSON] et al. [186] & Compressed air jet system & 15\% efficiency gain & - \\ \hline [PERSON] [187] & Robotic system with & & \\  & pneumatic suction & - & Cannot remove sticky dust \\ \hline [PERSON] [188] & Autonomous cleaning & - & Expensive, lacks quick \\  & vehicle & & movement flexibility \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of solar panel cleaning methods. Agent System (MAS), which makes decisions on how to manage the energy, like whether to use it, store it, or share it. It is designed to handle the unpredictable nature of solar power and changing energy needs. The whole setup is tested using a simulation in JADE, and the decisions are shown using LED lights, making it easy to test and later apply in real situations. This collaborative approach led to more efficient demand-side management. [PERSON] et al. [190] highlight in their work the integration of AI and the IoT in smart grid systems to enhance energy efficiency. IoT-enabled sensors collect real-time data on energy consumption, production, and environmental factors, allowing for the analysis of energy patterns and identification of inefficiencies. Using Bayesian optimization, a type of AI, the system can adaptively learn from historical data and real-time feedback to optimize energy consumption. This enables the smart grid to prioritize renewable energy sources, such as solar power, and dynamically adjust energy distribution. The combined use of IoT and AI improves grid performance, reduces energy waste, minimizes carbon emissions, and allows for proactive maintenance, making the grid more reliable and efficient. In South Korea, [PERSON] et al. [191] presented a real-world implementation of an AIoT-based solar-hydrogen energy management system at Kangwon National University's Samcheok Campus.