# Artificial Intelligence of Things for Solar Energy Monitoring and Control
###### Abstract  
In the rapidly evolving field of renewable energy, integrating Artificial Intelligence (AI) and the Internet of Things (IoT) has become a transformative strategy for improving solar energy monitoring and control. This paper provides a comprehensive survey of Artificial Intelligence of Things (AIoT) applications in solar energy, illustrating how IoT technologies enable real-time monitoring, system optimization through techniques such as Maximum Power Point Tracking (MPPT), solar tracking, and automated cleaning. Simultaneously, AI boosts these capabilities through energy forecasting, optimization, predictive maintenance, and fault detection, significantly enhancing system performance and reliability. This review highlights key advancements, challenges, and practical applications of AIoT in the solar energy sector, emphasizing its role in advancing energy efficiency and sustainability.  
artificial intelligence; internet of things; artificial intelligence of things; photovoltaic; fault detection and diagnosis; predictive maintenance; energy forecasting; solar energy monitoring; solar tracking; automated cleaning systems +
Footnote †: journal: Applied sciences  
0000-0002-4000-3899-2233][PERSON], A.M., 2000-0001-7998-2334]A.M.A.M., 2000-0002-1899-2334]D.J.E.E., 2000-0002-1899-2334]H.H.E.E.E.E.E.E.E.E.E.E.E.E.E.E.E.E.E.E.E.E  
monitoring and control systems are essential. These systems ensure optimal management of energy production and meet rising electricity demands. Solar energy monitoring relies on components such as sensors and microcontrollers that support real-time tracking and performance optimization [7]. Alongside monitoring, control systems are critical for adjusting panel operations dynamically based on real-time data, improving efficiency and responsiveness. Fault detection also plays a vital role in maintaining system performance and extending operational lifespan. Recent advancements have introduced intelligent and automated methods for identifying faults in PV systems. By using IoT-enabled monitoring devices, these technologies support real-time detection of issues, enhancing the overall reliability and effectiveness of solar energy systems [8]. Building upon IoT capabilities, AI introduces advanced data analytics to improve energy forecasting, optimize panel orientation, and reduce operational costs. AI algorithms analyze both real-time and historical data to anticipate weather patterns, detect faults, and schedule maintenance proactively. These capabilities help avoid system failures and improve the longevity of PV installations. Moreover, AI contributes to smart grid integration by accurately predicting energy demand, further improving the stability and scalability of solar power networks [9; 10; 11; 12; 13]. Together, AI and IoT--known as AIoT--represent a unified approach that combines intelligent data processing with connected devices to maximize the performance of solar energy systems.  
This survey examines the integration of AIoT in solar energy systems, focusing on IoT-enabled technologies for real-time monitoring, energy optimization through tracking and cleaning systems, and AI-driven applications for fault detection, predictive maintenance, and energy forecasting.