Predicting Solar Panel Efficiency using Machine Learning Approach

Abstract

This work focuses on predicting the efficiency of photovoltaic (PV) systems using machine learning techniques. Various environmental and technical factors such as solar irradiance, temperature, and system orientation were analyzed. Several machine learning models, including Linear Regression, Random Forest, XGBoost, and Support Vector Regression, were implemented and compared. Simulation tools like PVsyst and Python were used to prepare the dataset and evaluate model performance. Finally, a web application was developed to make the prediction results accessible and interactive. This study demonstrates how artificial intelligence can enhance the reliability and accuracy of solar energy systems.