Data-Driven Digital Twins of Renewable Energy Grids

This conference paper presents the concept of data-driven digital twins for renewable energy grids, with a focus on the application of artificial intelligence methods for modeling photovoltaic systems. The proposed data-driven digital twin system utilizes automated machine learning algorithms to generate accurate models of solar collectors, enabling real-time monitoring and forecasting of energy production. A use case of a solar power plant in a company from Čačak, Serbia is presented, demonstrating the potential of this technology to improve energy management and increase the efficiency of renewable energy sources. Results obtained from the use case demonstrate the effectiveness of this approach in optimizing energy management, reducing energy consumption, and improving the reliability of renewable energy systems.