Floating offshore wind turbines (FOWTs) exhibit complex motions under marine environmental loads and frequently undergo coupled oscillations across multiple degrees of freedom (DOFs). Accurate short-term motion prediction of these responses is crucial for operational safety and maintenance. To overcome the limitations of traditional “black-box” models under complex aero-hydrodynamic loads, this study proposes STL–TimesNet–NLinear, a novel physics-informed deep learning framework. The framework utilizes STL decomposition to explicitly decouple motion signals: NLinear captures non-stationary low-frequency slow drifts, while TimesNet extracts multi-periodic wave-frequency responses. The model was evaluated across different platform typologies—a 5 MW semi-submersible and a larger-scale 15 MW Spar-type platform—under various typical operational and extreme environmental conditions. Model performance was evaluated using comparative and ablation experiments. At a prediction-ahead time (PAT) of 5 s, the proposed model achieves coefficients of determination (R²) exceeding 0.95. Even at longer PATs, the R² remains above 0.90, consistently outperforming multiple benchmark models. Compared to traditional recurrent neural networks (e.g., LSTM), it decreases the Mean Absolute Error (MAE) for pitch motion under extreme sea states by 54.7% and increases the R² to 0.9573. Furthermore, the inference latency is only 2.4 ms per step. These findings confirm that the proposed STL–TimesNet–NLinear model provides fast and accurate solutions for the short-term motion response prediction of FOWTs, demonstrating valuable potential applications for enhancing the safety planning of offshore wind turbine operation and maintenance.

Floating offshore wind turbines platforms are critical components of offshore wind development. Accurately predicting their motion response is vital for ensuring platform safety, optimizing power generation efficiency, and reducing operation and maintenance costs. Therefore, drawing on advancements in deep learning, this study proposes a method utilizing the inverted Transformer (iTransformer) model to predict the short-term motion response of floating offshore wind turbines. The primary objective is to enhance prediction accuracy. First, numerical simulations of a specific 5 MW floating offshore wind turbines platform were conducted under various environmental conditions. Subsequently, the simulation data were preprocessed and utilized for experimental evaluation. Finally, to assess model performance, comparative analyses were conducted between the iTransformer model and mainstream recurrent neural network hybrid models currently used in this field. Specifically, long short-term memory-attention, gated recurrent units-attention, and bidirectional long short-term memory-attention models were compared under different predicted advance times. The results indicate that at a predicted advance time of 3 s, the iTransformer model achieves the highest accuracy, although the difference compared to the three recurrent neural network hybrid models is not significant. However, as the predicted advance time increases, the superiority of the iTransformer model becomes increasingly apparent. Specifically, under the EC1 condition at a predicted advance time of 9 s, compared with the best-performing recurrent neural network-attention baseline among the long short-term memory-attention model, gated recurrent units-attention model, and bidirectional long short-term memory-attention model, the iTransformer reduces the mean absolute error values of sway, roll, and pitch by 47.4%, 44.6%, and 43.2%, respectively. Furthermore, the model demonstrates a significant improvement in training speed. These findings suggest that the proposed model outperforms other models in terms of both prediction accuracy and training efficiency. Additionally, it offers an extended forecasting horizon. This study validates the potential of the iTransformer as a reliable tool for short-term motion prediction, providing robust support for planning the safe operation and maintenance of floating offshore wind turbines.