A Digital Twin Framework for PV Panels

The inability of PV panels to store the energy, changes in energy production due to weather conditions, and challenges in maintaining long-term efficiency create significant difficulties. In this study, we propose a comprehensive digital twin framework that integrates IoT, machine learning, mathematical modeling, and 3D visualization. To demonstrate the application of our framework, we conducted an experimental study focused on modeling the energy production of PV panels. To this end, we developed a mathematical model and 3D visualization and compared the performance of online and offline learning models. According to the machine learning results, LSTM was the most successful model (MSE: 0.0002). When online learning (MSE: 0.05) was compared with offline learning (MSE: 0.06), online learning demonstrated better performance in predicting energy production. The findings indicate that our digital twin framework could be a robust basis for energy management systems.