# Analysis, forecasting and system identification of a floating offshore wind turbine using dynamic mode decomposition

arXiv: https://arxiv.org/abs/2411.07263 · HTML: https://mlsanity.com/paper/2411.07263/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 101 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Deep learning for universal linear embeddings of nonlinear dynamics](https://mlsanity.com/paper/1712.09707/) (2017)
- [Modern Koopman Theory for Dynamical Systems](https://mlsanity.com/paper/2102.12086/) (2021)
- [Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition](https://mlsanity.com/paper/1710.04340/) (2017)
- [Linearly-Recurrent Autoencoder Networks for Learning Dynamics](https://mlsanity.com/paper/1712.01378/) (2017)
- [Dynamic mode decomposition with control](https://mlsanity.com/paper/1409.6358/) (2014)
- [Generalizing Koopman Theory to allow for inputs and control](https://mlsanity.com/paper/1602.07647/) (2016)
- [Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition](https://mlsanity.com/paper/1409.5496/) (2014)
- [Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems](https://mlsanity.com/paper/2010.04248/) (2020)
- [Predicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Method](https://mlsanity.com/paper/2405.08033/) (2024)
- [Feedforward control for wave disturbance rejection on floating offshore wind turbines](https://mlsanity.com/paper/2004.02931/) (2020)

## Built on by (most cited first, 3 of 3)

- [System Identification of a Moored ASV with Recessed Moon Pool via Deterministic and Bayesian Hankel-DMDc](https://mlsanity.com/paper/2511.03482/) (2025)
- [Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Hankel dynamic mode decomposition](https://mlsanity.com/paper/2511.04461/) (2025)
- [Equation-Free Digital Twins for Nonlinear Structural Dynamics](https://mlsanity.com/paper/2605.00950/) (2026)

## Abstract (arXiv)

This article presents the data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the application of dynamic mode decomposition (DMD). All the analyses are performed on experimental data collected from an operating prototype. The DMD has here used i) to extract knowledge from the dynamic system through its modal analysis, ii) for short-term forecasting from the knowledge of the immediate past of the system state, and iii) for the system identification and reduced order modeling. The forecasting method for the motions, accelerations, and forces acting on the floating system is developed using Hankel-DMD, a methodological extension that includes time-delayed copies of the states in an augmented state vector. The system identification task is performed by applying Hankel-DMD with control (Hankel-DMDc), which models the system including the effect of forcing terms. The influence of the main hyperparameters of the methods, namely the number of delayed copies in the state and input vector and the length of the observation time, is investigated with a full factorial analysis using three error metrics analyzing complementary aspects of the prediction: the normalized root mean square error, the normalized average minimum-maximum absolute error, and the Jensen-Shannon divergence. A Bayesian extension of the Hankel-DMD and Hankel-DMDc is introduced by considering the hyperparameters as stochastic variables varying in suitable ranges defined after the full factorial analysis, enriching the predictions with uncertainty quantification. Results show the capability of the approaches for short-term forecasting and system identification, suggesting their potential for real-time continuously-learning digital twinning and surrogate data-driven reduced order modeling.