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Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model

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  • Wei Sun

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

  • Qi Gao

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

Abstract

Wind power, one of renewable energy resources, is a fluctuating source of energy that prevents its further participation in the power market. To improve the stability of the wind power injected into the power grid, a short-term wind speed predicting model is proposed in this work, named VMD-P-(ARIMA, BP)-PSOLSSVM. In this model, variational mode decomposition (VMD) is combined with phase space reconstruction (P) as data processing method to determine intrinsic mode function (IMF) and its input–output matrix in the prediction model. Then, the linear model autoregressive integrated moving average model (ARIMA) and typical nonlinear model back propagation neural network (BP) are adopted to forecast each IMF separately and get the prediction of short-term wind speed by adding up the IMFs. In the final stage, particle swarm optimization least squares support vector machine (PSOLSSVM) uses the prediction results of the two separate models from previous step for the secondary prediction. For the proposed method, the PSOLSSVM employs different mathematical principles from ARIMA and BP separately, which overcome the shortcoming of using just single models. The proposed combined optimization model has been applied to two datasets with large fluctuations from a northern China wind farm to evaluate the performance. A performance comparison is conducted by comparing the error from the proposed method to six other models using single prediction techniques. The comparison result indicates the proposed combined optimization model can deliver more accurate and robust prediction than the other models; meanwhile, it means the power grid dispatching work can benefit from implementing the proposed predicting model in the system.

Suggested Citation

  • Wei Sun & Qi Gao, 2019. "Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model," Energies, MDPI, vol. 12(12), pages 1-27, June.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2322-:d:240643
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    References listed on IDEAS

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    Cited by:

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    2. Ren, Yuting & Li, Zhuolin & Xu, Lingyu & Yu, Jie, 2023. "The data-based adaptive graph learning network for analysis and prediction of offshore wind speed," Energy, Elsevier, vol. 267(C).
    3. Ming Pang & Lei Zhang & Yajun Zhang & Ao Zhou & Jianming Dou & Zhepeng Deng, 2022. "Ultra-Short-Term Wind Speed Forecasting Using the Hybrid Model of Subseries Reconstruction and Broad Learning System," Energies, MDPI, vol. 15(12), pages 1-21, June.
    4. Geng, Xiulin & Xu, Lingyu & He, Xiaoyu & Yu, Jie, 2021. "Graph optimization neural network with spatio-temporal correlation learning for multi-node offshore wind speed forecasting," Renewable Energy, Elsevier, vol. 180(C), pages 1014-1025.
    5. Dabin Zhang & Qian Li & Amin W. Mugera & Liwen Ling, 2020. "A hybrid model considering cointegration for interval‐valued pork price forecasting in China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1324-1341, December.

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