Abstract
Wind energy has become one of the most efficient renewable energy sources. However, the wind has the characteristics of intermittence and uncontrollability, so it is challenging to predict wind speed accurately. Considering the shortcomings of traditional wind power point predictions, a new hybrid model comprised three main modules used for data preprocessing, deterministic point prediction, and interval prediction is proposed to predict the wind speed interval. The first module, the data preprocessing module, uses variational mode decomposition (VMD), sample entropy (SE), and singular spectrum analysis (SSA) to extract the different frequency components of the initial wind speed series. The second module, the deterministic point prediction module, uses extreme learning machines (ELM), and a gated recursive unit (GRU) model to perform point prediction on the wind speed series. The third module, the interval prediction module, uses the nonparametric kernel density estimation method to construct the upper and lower bounds of the wind speed interval. In addition, the final wind speed prediction interval is obtained by integrating the prediction results of multiple interval prediction results to improve the robustness and generalization of the wind speed interval prediction. Finally, the effectiveness of the prediction performance of the proposed hybrid model is verified based on the data of two actual wind farms. The experimental results show that the proposed hybrid model can obtain the appropriate wind speed interval with high confidence and quality with different confidence levels of 95%, 90%, and 85%.
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Data availability
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
The authors thank State Grid Liaoning Electric Power Co., Ltd for collaboration on providing wind speed data.
Funding
This work was funded by the National Natural Science Foundation of China (Nos. 61873053 and 61973057) and the National Key Research and Development Program of China (Nos. 2019YFE0105000).
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XH: idea, data gathering, writing–original draft preparation, methodology, software. CY: investigation, draft reviewing, editing, supervision. ZY: methodology, software. WF draft reviewing, editing.
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Xu, H., Chang, Y., Zhao, Y. et al. A novel hybrid wind speed interval prediction model based on mode decomposition and gated recursive neural network. Environ Sci Pollut Res 29, 87097–87113 (2022). https://doi.org/10.1007/s11356-022-21904-5
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DOI: https://doi.org/10.1007/s11356-022-21904-5