Revolutionizing Photovoltaic Asset Performance with Digital Twin and AI

Managing and maintaining photovoltaic (PV) assets presents a significant challenge. Ensuring optimal performance, minimizing downtime, and maximizing energy production are critical objectives. However, current methods often lack timely insights, making it difficult to diagnose issues promptly and plan maintenance effectively. This research provides an integrated strategy that combines a machine learning (ML) model with a digital twin architecture to improve solar panel monitoring and maintenance. A camera fixed on a solar panel serves as the focal point of the system, capturing images whenever the software detects that current generation falls below a threshold value. These images are processed by an ML model trained to determine the condition of the panel—specifically, whether it is dusty, clean, damaged, or intact. By combining the output of the ML model with real-time weather data, the underlying reason for reduced current generation is inferred. The principal aim is to incorporate both the current condition of the solar panels and the prevailing weather into a digital replica of the solar power plant, thereby facilitating enhanced and more efficient maintenance and operational strategies. Leveraging digital twins, continuous data collection, and machine learning enables improved lifecycle management of photovoltaic systems. This approach represents a meaningful advancement in applying technology to make solar energy more reliable and efficient.