<![CDATA[The rapid expansion of solar photovoltaic (PV) technologies has increased the demand for intelligent, adaptive, and data-driven energy management systems. However, conventional and IoT only solar infrastructures still face limitations, including inefficient energy distribution, delayed fault detection, and an inability to respond dynamically to fluctuating environmental conditions. This study proposes an AIoT-based Smart Solar System that integrates IoT-enabled sensing modules with artificial intelligence for real-time monitoring, predictive analytics, and autonomous control. The system employs a distributed architecture consisting of edge devices, cloud analytics, and machine learning models particularly Long Short-Term Memory (LSTM) networks and regression-based predictors to enhance forecasting accuracy and operational responsiveness. The objective of this research is to improve power utilization, predictive reliability, and maintenance efficiency within solar energy systems. Experimental results demonstrate a 22.8% increase in power utilization, a 17% reduction in maintenance downtime, and a forecasting accuracy of 95.2% (R2 = 0.952). These findings indicate that AIoT integration significantly enhances energy intelligence, system reliability, and sustainability. Overall, the proposed architecture establishes a scalable foundation for next generation renewable energy systems capable of self learning, adaptive optimization, and real-time decision making.]]>
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