Artificial Intelligence and Integrated Optimization in the Energy Sector: Advances in Photovoltaic System

The integration of artificial intelligence and digital twins in the energy sector is revolutionizing the industry, especially how solar plants are monitored and managed. Digital twins create virtual representations of physical systems, enabling early fault detection, performance optimization, and predictive simulations of operational scenarios. AI enhances these capabilities by leveraging machine learning algorithms that analyze real-time data to improve fault prediction and preventive maintenance. This article reviews both Machine-Learning and physics-based approaches for implementing digital twins in photovoltaic systems, highlighting their calibration using machine learning techniques. Furthermore, it explores current AI applications in system optimization, fault diagnosis, and predictive maintenance for photovoltaic plants. Finally, emerging trends are discussed, particularly the convergence of these technologies with the Internet of Things and intelligent automation, aiming to enhance the efficiency and reliability of solar energy generation.