IoT Based Dual Axis Solar Tracker and Irradiation Forecasting Using Machine Learning

This work presents the development of a Dual axis solar tracker along with an IoT-based fully automated data logger and a computer program for the prediction of solar irradiance. The proposed work aims to measure, record, analyse the performance of the solar irradiance all day without the use of traditional methods and any human effort and further use the stored data for the solar irradiance forecasting for the forth-coming months by using eXtreme Gradient Boosting(XGBoost) machine learning algorithm. The hardware includes a pair of Arduino Uno microcontroller, a current sensor, voltage sensor, relay and Wi-Fi module along with the two servo motors acting as actuators, Light- Dependent Resistor (LDRs) are used as light detection sensors which detects the solar rays. Further, the motor drives the solar panel towards the position of maximum incoming light. The IoT platforms “Thingspeak” and “IFTTT” are used to record and track performance of the solar irradiance respectively.