A Hybrid Framework for Offshore Wind Power Forecasting: Integrating CNN-BiGRU-XGBoost with Advanced Feature Engineering and Analysis

This paper proposes a hybrid forecasting model for offshore wind power, combining CNN, BiGRU, and XGBoost to address the challenges of fluctuating wind speeds and complex meteorological conditions. The model extracts local and temporal features, models nonlinear relationships, and uses residual-driven Ridge regression for improved error correction. Real-world data from a Jiangsu offshore wind farm in 2023 was used for training and testing. Results show the proposed approach consistently outperforms traditional models, achieving lower RMSE and MAE, and R2 values above 0.98 across all seasons. While the model shows strong robustness and accuracy, future work will focus on optimizing hyperparameters and expanding input features for even broader applicability. Overall, this hybrid model provides a practical solution for reliable offshore wind power forecasting.