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A new fusion model for enhanced ultra-short-term offshore wind power forecasting

Author

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  • Wang, Qiang
  • Xu, Feiyan
  • He, Jiahua
  • Luo, Kun
  • Fan, Jianren

Abstract

Wind power generation depends on meteorological conditions, causing fluctuations that affect power system stability. Accurate ultra-short-term forecasting of wind farm power is essential for reliable turbine operational control. However, current prediction accuracy falls short of these requirements. To this end, this study establishes an ultra-short-term power prediction fusion model tailored for offshore wind farms. Integrating ensemble empirical mode decomposition (EEMD), Bayesian optimization (BO), gated recurrent unit (GRU), and bidirectional GRU (BiGRU), the proposed fusion model, EEMD-BO-BiGRU, effectively reduces the fluctuation of the original power series. The overall performance is evaluated using the data from an offshore wind farm in Hangzhou Bay, China. The results show that the EEMD-BO-BiGRU fusion model achieves a qualification rate of 95.56 % in ultra-short-term power forecasting for the offshore wind farm. The improvement rates of BO, GRU, and BiGRU are 14.62 %, 9.97 %, and 7.44 %, respectively. A comparison of the prediction performance across different areas of the wind farm indicates that, compared to the wake region, the model performs best in forecasting for upstream wind turbines. This fusion model offers improved technical support for ultra-short-term power prediction and contributes to the real-time operation and control of offshore wind farms.

Suggested Citation

  • Wang, Qiang & Xu, Feiyan & He, Jiahua & Luo, Kun & Fan, Jianren, 2026. "A new fusion model for enhanced ultra-short-term offshore wind power forecasting," Renewable Energy, Elsevier, vol. 256(PA).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pa:s096014812501540x
    DOI: 10.1016/j.renene.2025.123876
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    1. Jicheng Quan & Li Shang, 2020. "Short-Term Wind Speed Forecasting Based on Ensemble Online Sequential Extreme Learning Machine and Bayesian Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, November.
    2. Yang, Mao & Huang, Yutong & Guo, Yunfeng & Zhang, Wei & Wang, Bo, 2024. "Ultra-short-term wind farm cluster power prediction based on FC-GCN and trend-aware switching mechanism," Energy, Elsevier, vol. 290(C).
    3. Men, Zhongxian & Yee, Eugene & Lien, Fue-Sang & Wen, Deyong & Chen, Yongsheng, 2016. "Short-term wind speed and power forecasting using an ensemble of mixture density neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 203-211.
    4. Bommidi, Bala Saibabu & Teeparthi, Kiran & Kosana, Vishalteja, 2023. "Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function," Energy, Elsevier, vol. 265(C).
    5. Daniel Vassallo & Raghavendra Krishnamurthy & Thomas Sherman & Harindra J. S. Fernando, 2020. "Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting," Energies, MDPI, vol. 13(20), pages 1-19, October.
    6. Qiu, Hong & Shi, Kaikai & Wang, Renfang & Zhang, Liang & Liu, Xiufeng & Cheng, Xu, 2024. "A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects," Renewable Energy, Elsevier, vol. 227(C).
    7. Xu, Yuanyuan & Yang, Genke & Luo, Jiliang & He, Jianan & Sun, Haixin, 2022. "A multi-location short-term wind speed prediction model based on spatiotemporal joint learning," Renewable Energy, Elsevier, vol. 183(C), pages 148-159.
    8. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
    9. Wang, Yaqi & Zhao, Xiaomeng & Li, Zheng & Zhu, Wenbo & Gui, Renzhou, 2024. "A novel hybrid model for multi-step-ahead forecasting of wind speed based on univariate data feature enhancement," Energy, Elsevier, vol. 312(C).
    10. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
    11. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
    12. Zhang, Dongdong & Chen, Baian & Zhu, Hongyu & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model," Energy, Elsevier, vol. 285(C).
    13. Yadong Lei & Zhili Wang & Deying Wang & Xiaoye Zhang & Huizheng Che & Xu Yue & Chenguang Tian & Junting Zhong & Lifeng Guo & Lei Li & Hao Zhou & Lin Liu & Yangyang Xu, 2023. "Co-benefits of carbon neutrality in enhancing and stabilizing solar and wind energy," Nature Climate Change, Nature, vol. 13(7), pages 693-700, July.
    14. Liang, Yang & Zhang, Dongqin & Zhang, Jize & Hu, Gang, 2024. "A state-of-the-art analysis on decomposition method for short-term wind speed forecasting using LSTM and a novel hybrid deep learning model," Energy, Elsevier, vol. 313(C).
    15. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2021. "A novel genetic LSTM model for wind power forecast," Energy, Elsevier, vol. 223(C).
    16. Shahram Hanifi & Saeid Lotfian & Hossein Zare-Behtash & Andrea Cammarano, 2022. "Offshore Wind Power Forecasting—A New Hyperparameter Optimisation Algorithm for Deep Learning Models," Energies, MDPI, vol. 15(19), pages 1-21, September.
    17. Zhou, Yilin & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2022. "Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    18. Mingqiu Liu & Zhichang Liang & Haixiao Liu, 2022. "Numerical Investigations of Wake Expansion in the Offshore Wind Farm Using a Large Eddy Simulation," Energies, MDPI, vol. 15(6), pages 1-19, March.
    19. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    20. Hu, Yahui & Guo, Yingshi & Fu, Rui, 2023. "A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network," Energy, Elsevier, vol. 278(PA).
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