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A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections

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  • Liu, Hui
  • Yang, Rui
  • Wang, Tiantian
  • Zhang, Lei

Abstract

Under the dual stimulus of the new energy demand and the increasing competitiveness of wind energy, the construction of wind speed prediction models began to be placed in a position that cannot be ignored. To overcome the challenges brought by wind speed fluctuations to wind speed forecasting, this paper proposes a novel hybrid wind speed forecasting deep model. The model has three modules, including data preprocessing, multi-learner ensemble, and adaptive multiple error correction. We used four real wind series in Xinjiang, China to verify the performance of the model. The results of the case study show that: (a) The proposed hybrid deep model for wind speed forecasting is superior to several state-of-the-art models in terms of both forecasting stability and forecasting accuracy; (b) The proposed hybrid deep model is excellent in multi-step forecasting, taking the site #1 as an example, the MAEs of the proposed model are 0.0250 m/s, 0.0417 m/s, and 0.0570 m/s, respectively.

Suggested Citation

  • Liu, Hui & Yang, Rui & Wang, Tiantian & Zhang, Lei, 2021. "A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections," Renewable Energy, Elsevier, vol. 165(P1), pages 573-594.
    • Handle: RePEc:eee:renene:v:165:y:2021:i:p1:p:573-594
    DOI: 10.1016/j.renene.2020.11.002
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