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A Short-Term Wind Speed Forecasting Framework Coupling a Maximum Information Coefficient, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Shared Weight Gated Memory Network with Improved Northern Goshawk Optimization for Numerical Weather Prediction Correction

Author

Listed:
  • Yanghe Liu

    (Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China)

  • Hairong Zhang

    (Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China)

  • Chuanfeng Wu

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Mengxin Shao

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Liting Zhou

    (Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China)

  • Wenlong Fu

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China)

Abstract

In line with global carbon-neutral policies, wind power generation has received widespread public attention, which can enhance the security of supply and social sustainability. Since wind with non-stationarity and randomness makes power systems unstable, precise wind speed forecasting is an integral part of wind farm scheduling and management. Therefore, a compound short-term wind speed forecasting framework based on numerical weather prediction (NWP) is proposed coupling a maximum information coefficient (MIC), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), shared weight gated memory network (SWGMN) with improved northern goshawk optimization (INGO). Firstly, numerical weather prediction is adopted to acquire the predicted variables with different domains, including the predicted wind speed and other predicted meteorological variables, after which the error is calculated using the predicted and actual wind speeds. Then, the correlation between the predicted variables and the error is obtained using the MIC to select the correlation factors. Subsequently, CEEMDAN is employed to decompose the correlation factors, corresponding the actual factors and the error into a series of subsequences, which are regarded as the input series. After that, the input series is fed into the proposed SWGMN to forecast each subsequent error, respectively, in which the shared gate is proposed to replace the input gate, the forgetting gate and the output gate. Meanwhile, the proposed INGO based on northern goshawk optimization (NGO), the levy flight disturbance strategy and the nonlinear contraction strategy is applied to calibrate the parameters of the SWGMN. Finally, the forecasting values are acquired by summing the forecasted error and the predicted wind speed from the NWP. The experimental results depict that the errors are small among all the models. Compared with the traditional method, the proposed framework achieves higher prediction accuracy and efficiency. The application of this framework not only assists in optimizing the operation and management of wind farms, but also reduces the dependence on fossil fuels, thereby promoting environmental protection and the sustainable use of resources.

Suggested Citation

  • Yanghe Liu & Hairong Zhang & Chuanfeng Wu & Mengxin Shao & Liting Zhou & Wenlong Fu, 2024. "A Short-Term Wind Speed Forecasting Framework Coupling a Maximum Information Coefficient, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Shared Weight Gated Memory Network with Im," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6782-:d:1451942
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    References listed on IDEAS

    as
    1. Fu, Wenlong & Fu, Yuchen & Li, Bailing & Zhang, Hairong & Zhang, Xuanrui & Liu, Jiarui, 2023. "A compound framework incorporating improved outlier detection and correction, VMD, weight-based stacked generalization with enhanced DESMA for multi-step short-term wind speed forecasting," Applied Energy, Elsevier, vol. 348(C).
    2. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    3. Wang, Jian & Yang, Zhongshan, 2021. "Ultra-short-term wind speed forecasting using an optimized artificial intelligence algorithm," Renewable Energy, Elsevier, vol. 171(C), pages 1418-1435.
    4. Wang, Can & Wang, Zhen & Chu, Sihu & Ma, Hui & Yang, Nan & Zhao, Zhuoli & Lai, Chun Sing & Lai, Loi Lei, 2024. "A two-stage underfrequency load shedding strategy for microgrid groups considering risk avoidance," Applied Energy, Elsevier, vol. 367(C).
    5. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
    6. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(C).
    7. Zhang, Chu & Hu, Haowen & Ji, Jie & Liu, Kang & Xia, Xin & Nazir, Muhammad Shahzad & Peng, Tian, 2023. "An evolutionary stacked generalization model based on deep learning and improved grasshopper optimization algorithm for predicting the remaining useful life of PEMFC," Applied Energy, Elsevier, vol. 330(PA).
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