AI-Powered Solar Panel Cleaning Scheduler :Detecting Efficiency Drop, Dust Levels & Automating Scheduling

The accumulation of dust and pollutants on solar panels significantly reduces their energy conversion efficiency, resulting in performance degradation and increased maintenance costs. This paper presents an AIpowered solar panel cleaning scheduler that autonomously detects dust accumulation and efficiency drops, and dynamically schedules cleaning operations. The proposed system integrates a solar-powered mobile rover equipped with an ultrasonic sensor, motor driver, DC motor, and real-time clock (RTC) for intelligent monitoring and timebased operation. The rover traverses the panel surface using a motor-driven mechanism to perform automated cleaning whenever a predefined drop in efficiency or increase in dust level is detected. The AI module, trained on real-time efficiency and environmental data, predicts the optimal cleaning interval by analyzing sunlight intensity, dust density, and power output trends. A microcontroller-based control unit coordinates sensor inputs, rover movement, and cleaning commands, while the 10V solar battery provides sustainable energy for system operation. Experimental validation demonstrates that the proposed design effectively maintains panel efficiency and minimizes unnecessary cleaning cycles, leading to improved energy yield and reduced water and labor usage. This work highlights the potential of combining AI, IoT sensing, and robotic automation for the development of self-sustaining, intelligent solar maintenance systems.