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Multi-step short-term wind speed prediction based on integrated multi-model fusion

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  • Tian, Zhongda
  • Chen, Hao

Abstract

Wind power generation is uncontrollable and non-adjustable green energy. The accurate prediction of wind speed is of great significance to the operation and maintenance of wind farms and the safety of the power grid. A novel wind speed prediction approach based on mode decomposition combined with integrated multi-model fusion is proposed. The purpose of the integrated multi-model fusion method is to improve the accuracy of wind speed prediction. Two decomposition algorithms, empirical mode decomposition and local mean decomposition are used to decompose the wind speed into several components respectively. After decomposition, the odd and even sequences are extracted to form two new sequences. According to the characteristics of the components obtained from the decomposition of odd and even sequences, stochastic configuration network and support vector machine are selected as the prediction models. Meanwhile, the particle swarm optimization algorithm is introduced to optimize the parameters of support vector machine. After all decomposition components of the odd and even sequence are predicted by stochastic configuration networks and support vector machine, the predicted values are superimposed to obtain the final predicted value of the odd and even sequence. Finally, the predicted values of odd and even series are integrated to get the final predicted value of wind speed. By comparing the actual and predicted values of short-term wind speed, RMSE, R2, MAE, MAPE, Pearson’s test, prediction error distribution box-plot, and Taylor diagram are used to judge the performance indicators of the prediction model. The case study results show that, compared with the state-of-the-art prediction models, RMSE decreases by about 0.1, MAPE decreases by about 0.05%, MAE decreases by about 0.2, and R2 increases by about 0.2. The other performance indicators of the proposed approach are also better than other models, which prove that the integrated multi-model fusion model can improve the accuracy and effectiveness of wind speed prediction. The proposed prediction approach has high prediction accuracy, can correctly reflect the short-term wind speed law, and has a good application prospect.

Suggested Citation

  • Tian, Zhongda & Chen, Hao, 2021. "Multi-step short-term wind speed prediction based on integrated multi-model fusion," Applied Energy, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921006681
    DOI: 10.1016/j.apenergy.2021.117248
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    1. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
    2. Zhang, Wenyu & Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong, 2020. "Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting," Applied Energy, Elsevier, vol. 277(C).
    3. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    4. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    5. Zhao, Jing & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Zhu, Wenjin, 2021. "A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions," Energy, Elsevier, vol. 218(C).
    6. 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.
    7. 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).
    8. Zhang, Jinliang & Wei, Yiming & Tan, Zhongfu, 2020. "An adaptive hybrid model for short term wind speed forecasting," Energy, Elsevier, vol. 190(C).
    9. Hu, Yaogang & Li, Hui & Shi, Pingping & Chai, Zhaosen & Wang, Kun & Xie, Xiangjie & Chen, Zhe, 2018. "A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process," Renewable Energy, Elsevier, vol. 127(C), pages 452-460.
    10. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    11. Xin Zhao & Haikun Wei & Chenxi Li & Kanjian Zhang, 2020. "A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed," Energies, MDPI, vol. 13(7), pages 1-15, April.
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    6. Li, Jiale & Song, Zihao & Wang, Xuefei & Wang, Yanru & Jia, Yaya, 2022. "A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD," Energy, Elsevier, vol. 251(C).

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