Machine Learning-Enhanced Dual Axis Solar Tracking with Weather Monitoring for Rural Electrification

The proposed dual-axis solar tracking system integrates real-time weather monitoring and machine learning to enhance solar energy efficiency. Machine learning algorithms analyze historical weather data alongside real-time sensor readings, which is crucial for optimizing solar energy capture, as demonstrated in various studies on solar tracking systems. The integration of a mobile application interface allows users to access real-time data and receive alerts, facilitating immediate adjustments to the system settings. This aligns with advancements in remote monitoring technologies, such as those used in microalgal productivity prediction, which leverage real-time sensor data for operational forecasting. Furthermore, the use of the Blynk IoT platform for remote control enhances user engagement and system responsiveness, reflecting a growing trend in smart energy management solutions. Overall, this project exemplifies the convergence of renewable energy technology, machine learning, and IoT, promising improved operational reliability and energy efficiency.