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Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism

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  • Zhang, Shuai
  • Chen, Yong
  • Xiao, Jiuhong
  • Zhang, Wenyu
  • Feng, Ruijun

Abstract

Accurate and reliable wind speed forecasting is important for the dispatch and management of wind power generation systems. However, existing forecasting models based on the data decomposition approach only perform time–frequency analysis of wind speed series, while ignoring the coupling relationship between other meteorological variables and wind speed in the time and frequency domains. Therefore, a novel hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with an attention mechanism is proposed in this study. Specifically, singular spectrum analysis is used to decrease the noise of the original multivariate series. Multivariate empirical mode decomposition is used to decompose the denoised series into their respective intrinsic mode functions and residuals. Further, a new hybrid deep learning algorithm, which combines a convolutional neural network optimized via an attention mechanism and a bidirectional long short-term memory network, is proposed to extract spatiotemporal correlation features between all intrinsic mode functions and residuals and to perform final wind speed forecasting. Finally, three experiments were performed to evaluate the performance of the proposed model comprehensively. The experimental results indicate that the proposed model is superior to other baseline models in terms of both accuracy and effectiveness.

Suggested Citation

  • Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    • Handle: RePEc:eee:renene:v:174:y:2021:i:c:p:688-704
    DOI: 10.1016/j.renene.2021.04.091
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    References listed on IDEAS

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