A research team led by scientists from the United Kingdom’s Cranfield University has developed an AI-driven digital twin framework for FPV systems. The system uses a physical FPV twin deployed on a water surface, with sensors transmitting data to the cloud and ultimately to the digital version of the installation.
“The digital twin operates in the cloud environment, where AI models are employed to simulate system behavior and forecast performance under varying environmental conditions,” the scientists explained. “Crucially, the system does not merely react to anomalies after they occur. Instead, it enables predictive warning capabilities, identifying potential risks in advance based on trend analysis and learned patterns.”
The digital twin framework was developed using 155 physical experiments in Cranfield University’s wave tank, which measures 30 meters in length, 1.5 meters in width, and 1.5 meters in depth. A catamaran-type floating structure was placed centrally in the tank, carrying a 50 W solar panel powered by a solar simulator mounted 40 cm above it.
Sensors collected time-series data on hydrodynamic motion (heave, surge, pitch), mooring line forces, PV surface temperature, and power output. The dataset, sampled at 10 Hz, formed the empirical basis for training and validating the digital twin models. The experiments included five incidence angles (90°, 75°, 60°, 45°, 30°), two wave amplitudes (0.025 m and 0.0375 m), and 15 wavelengths from 1.5 m to 5.0 m in 0.25 m increments.
The resulting data was transferred to a two-tier ANN: a high-fidelity (strong) model and a reduced-order (light) model.
“In the initial stage, a high-fidelity (strong) neural network model is trained using experimental data as input. This model captures the complex physical behavior of the FPV system,” the team explained. “To address the computational demands of the high-fidelity model, a reduced-order (light) model is developed in the second stage. This model is trained on a curated dataset composed of selected outputs from the high-fidelity model, along with supplemental experimental data, enabling it to replicate core system behaviors with significantly lower computational overhead. In the final stage, the trained reduced-order model is integrated into a user-facing digital twin application.”
The high-fidelity model achieved R2 values of 0.9996 for PV surface temperature and 0.9189 for power output. It accurately captured oscillatory behaviours in surge and pitch motions, reproduced rapid variations in mooring forces, and transient power fluctuations, with root mean square errors (RMSEs) as low as 0.1986 W for power and 0.1526° for PV temperature. The reduced-order model retained strong performance with R² values of 0.9073 for power and 0.9660 for temperature.
“Three-dimensional performance maps generated by the trained models revealed strong nonlinear interactions between environmental inputs and system behavior,” the group concluded. “For instance, heave motion peaked under wave lengths of 2.5–3.5 m and higher amplitudes (~0.0375 m), indicating resonant conditions. Power output was maximized when solar irradiance exceeded 340 W/m² at a 90° incidence angle, and PV temperature exceeded 75°C under the same conditions. These insights enable predictive optimization and enhance understanding of FPV performance under variable sea states.”
The researchers presented the system in “Digital twins for a floating photovoltaic system with experimental data mining and artificial intelligence modelling,” which was recently published in Solar Energy. Scientists from Cranfield University and Beijing University of Posts and Telecommunications participated in the study.
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