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Improving short-term offshore wind speed forecast accuracy using a VMD-PE-FCGRU hybrid model

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  • Gong, Zhipeng
  • Wan, Anping
  • Ji, Yunsong
  • AL-Bukhaiti, Khalil
  • Yao, Zhehe

Abstract

The integration of large-scale offshore wind power into the power grid presents significant challenges for grid operation and dispatch due to the variability and intermittency of offshore wind energy. However, non-stationarities and noise in wind speed time series pose challenges. This study proposes a VMD-PE-FCGRU methodology to address this challenge of predicting offshore wind speeds. Firstly, the Variational Mode Decomposition (VMD) algorithm decomposes the original wind speed signal, considering the strong fluctuations and high noise inherent to offshore wind energy. A corresponding performance evaluation index is established to assess the effectiveness of this deconstruction process. To further enhance the model's (GRU) ability to capture time series information, a Position Encoding layer (PE) has been added, and the network is deepened through a Fully Connected Neural Network (FCNN). This allows multi-dimensional time series features to be input into the Gated Recurrent Unit (GRU) network for forecast. The accuracy of the proposed method is then verified using measured historical data from the wind tower of an offshore wind farm in Guangdong. Experimental results demonstrate that the proposed method can accurately forecast short-term wind speeds for offshore wind farms, with a Mean Absolute Error (MAE) of 0.199 and a Mean Absolute Percentage Error (MAPE) of only 2.45%. Moreover, the qualified rate (r) reaches 100%, providing an accurate reference for real-time power grid dispatching and can inform decision-making for the integration of offshore wind power into the grid.

Suggested Citation

  • Gong, Zhipeng & Wan, Anping & Ji, Yunsong & AL-Bukhaiti, Khalil & Yao, Zhehe, 2024. "Improving short-term offshore wind speed forecast accuracy using a VMD-PE-FCGRU hybrid model," Energy, Elsevier, vol. 295(C).
    • Handle: RePEc:eee:energy:v:295:y:2024:i:c:s0360544224007886
    DOI: 10.1016/j.energy.2024.131016
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    References listed on IDEAS

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    1. Zheng, Jingwei & Wang, Jianzhou, 2024. "Short-term wind speed forecasting based on recurrent neural networks and Levy crystal structure algorithm," Energy, Elsevier, vol. 293(C).
    2. Hu, Huanling & Wang, Lin & Tao, Rui, 2021. "Wind speed forecasting based on variational mode decomposition and improved echo state network," Renewable Energy, Elsevier, vol. 164(C), pages 729-751.
    3. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
    4. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    5. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    6. Pierre-Julien Trombe & Pierre Pinson & Henrik Madsen, 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations," Energies, MDPI, vol. 5(3), pages 1-37, March.
    7. Wang, Xuguang & Li, Xiao & Su, Jie, 2023. "Distribution drift-adaptive short-term wind speed forecasting," Energy, Elsevier, vol. 273(C).
    8. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
    9. Jin, Xiaoyu & Xiao, Fu & Zhang, Chong & Chen, Zhijie, 2022. "Semi-supervised learning based framework for urban level building electricity consumption prediction," Applied Energy, Elsevier, vol. 328(C).
    10. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).
    11. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
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