IoT Enabled Smart Solar Tracking Systems with Artificial Intelligence Emerging Trends, Challenges, and Future Directions

Abstract

The development of intelligent solar energy management systems has increased due to the growing demand for renewable energy and the requirement for improved photovoltaic (PV) efficiency. Dust buildup, shifting sunlight angles, and ineffective monitoring systems cause conventional solar panels in fixed positions to extract minimum energy. This work suggests an IoT, Raspberry Pi and AI-based smart solar tracking with monitoring system, as a solution to these problems. To optimize solar power production, the suggested system combines dual-axis sun tracking, wireless monitor-ing, real-time sensing, and automated cleaning. Sensors are used to continually monitor electrical and environmental factors such panel voltage, current, power, temperature, and light intensity. The main controller for data collection, processing, and actuator control is a Raspberry Pi 3B+. In order to maximize sunshine exposure, the system dynamically modifies panel orientation using twin DC motors based on inputs from LDR and BH1750 sensors. Remote monitoring via a web dashboard is proposed by IoT connectivity. According to experimental findings, tracking mode produced an average output voltage of 12.24 V as opposed to 11.35 V in stationary mode, a 7.82% improvement is observed. Additionally, the suggested approach minimized dust-related efficiency losses through automated cleaning and average power generation increased by about 18.6%. Smart maintenance recommendations, anomaly detection, and performance prediction are further presented by AI-based analytics. The findings show that Intelli Solar offers next-generation smart photovoltaic energy systems an effective, affordable, and scalable solution.