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

Yongguo Li, Jiayi Pan () and Jiangdong Wang
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Yongguo Li: Shanghai Engineering Research Center of Marine Renewable Energy, Shanghai 201306, China
Jiayi Pan: College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
Jiangdong Wang: College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China

Energies, 2025, vol. 18, issue 19, 1-19

Abstract: 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 R 2 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.

Keywords: offshore wind power; power forecasting; CNN-BiGRU-XGBoost; deep learning; hybrid modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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