IoT and Machine Learning Based Smart Solar Energy Monitoring and Automatic Dual-Axis Solar Tracking System

Abstract: Solar energy is one of the most promising renewable energy sources, yet its efficient utilization remains challenging due to fixed panel orientations, lack of real-time performance monitoring, and absence of intelligent data analysis. This paper presents an IoT and Machine Learning (ML) based Smart Solar Energy Monitoring and Automatic Dual-Axis Solar Tracking System. The proposed system integrates an Arduino UNO microcontroller and an ESP32 module with voltage, current (ACS712), and Light Dependent Resistor (LDR) sensors to continuously measure solar panel output parameters and automatically adjust panel orientation for maximum sunlight absorption. Sensor data comprising voltage, current, power, and energy generation is transmitted to the ThingSpeak cloud platform via Wi-Fi for real-time remote visualization. A dual-axis tracking mechanism employing four LDR sensors and two servo motors ensures continuous alignment of the solar panel with the sun, enhancing energy capture by approximately 30–40% compared to fixed installations. A Linear Regression-based machine learning model developed in Python predicts daily and monthly energy production and estimates potential income, enabling proactive energy management. A Streamlit- based web dashboard provides an interactive interface for real-time and historical data analysis. Experimental results confirm accurate parameter detection, effective solar tracking, reliable cloud data transmission, and practical income prediction. The system is cost-effective, scalable, and suitable for residential rooftop installations, smart solar farms, and educational applications.

Keywords: IoT, Arduino UNO, ESP32, solar energy monitoring, dual-axis solar tracking, LDR sensor, ACS712, ThingSpeak, machine learning, linear regression, Streamlit dashboard.