<![CDATA[Ranging from monitoring in real-time to predictive maintenance and operational optimization, the increasing complexity of renewable energy systems requires sophisticated solutions. The article proposes a holistic solution that combines drones and IoT to improve installation efficiency and reduce incidents in wind, solar, and hydropower energy production. The study uses a hybrid approach that combines sensor analytics, drone-assisted infrastructure inspection, edge computing for latency minimization, and multivariate modeling to quantify the system's enhancement. Field trials involved three renewable power plants over the course of six months and included the acquisition of more than 10,000 data related to power plant operations. It was shown that integrating a thermal, an RGB, and a LiDAR sensor on a drone resulted in a significant increase in inspection efficiency, fault coverage, and spatial resolution. At the same time, deployed IoT sensors continuously monitor inverter temperature, vibration frequency, and energy output. Statistical regression models revealed highly significant relationships among the frequency of UAV inspection, IoT latency, and energy efficiency, and algorithmic modules, such as support vector machines, Kalman filters, and ant colony optimization, further improved fault diagnosis, data fusion, and pathfinding. The results validate the applicability of drones and IoT for enhancing system uptime, dependability, and predictability without introducing extra operational load. This work lays out a scalable, modular approach, feasible for deployment in smart grid scenarios, which enables sustainable, intelligent energy management.]]>
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