Enhancing photovoltaic system efficiency through a digital twin framework: A comprehensive modeling approach
Abdul-Kadir Hamid, Mena Maurice Farag, Mousa Hussein
Abstract
Photovoltaic (PV) systems contribute significantly to renewable energy generation, but their efficiency and reliability are often hindered by environmental conditions, thermal inefficiencies, and a lack of predictive operational insights. Existing solutions, such as advanced material design, cooling systems, artificial intelligence-based modeling, Internet of Things, address these limitations to some extent but often focus on isolated and limited aspects. This study introduces a new Digital Twin framework that integrates physical modeling based on MATLAB Simulink environment, analytical formulations, and artificial intelligence-based Gradient Boosting Regression Trees. Real-time data from an established on-grid 2.88 kW PV system is utilized to validate the framework, ensuring practical applicability and accuracy. Unlike traditional methods, this comprehensive approach enables real-time monitoring, predictive maintenance, and operational optimization under varying environmental conditions. The findings demonstrate significant improvements in system performance, showcasing enhanced predictive accuracy of 99.77% and dynamic adaptability. Through the utilization of real-time data extracted from the established PV system, the framework provides a cost-effective solution for modeling large PV systems, ensuring practical and sustainable energy management with optimal operation.