What question did this study set out to answer?

The aim is to develop a digital twin tool for predicting the performance of floating photovoltaic systems in dynamic aquatic environments.

Real-time machine learning-driven digital twin framework of a floating solar system in waves

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Real-time digital twin framework improves operational resilience in floating solar systems, suggesting advanced offshore monitoring solutions.

The aim is to develop a digital twin tool for predicting the performance of floating photovoltaic systems in dynamic aquatic environments.
Developed a digital twin tool integrating laboratory tests and machine learning techniques.
Conducted 155 tests under varying irradiance and wave conditions.
Trained a Random Forest model to predict the dynamic responses of the floating systems.
Created a Unity-based interface for real-time visualization and interaction.
Achieved a predictive accuracy of R² = 0.990 with the Random Forest model.
Accurately predicted heave, surge, and pitch responses with minimal discrepancies.
Provided an interactive platform for monitoring FPV system behavior in real-time.

Yang et al. (2026) studied this question.

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Real-time digital twin framework improves operational resilience in floating solar systems, suggesting advanced offshore monitoring solutions.