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A novel paradigm for multi-step wind speed prediction: A hybrid system based on decomposition and weighted ensemble approach enhanced by Gaussian Kernel Function

Author

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  • Dong, Zhaochen
  • Tian, Zhirui
  • Lv, Shuang

Abstract

Wind power generation is an established and widely recognized form of clean energy power generation that is highly regarded by all nations worldwide. Nowadays, the precision of ultra-short-term (ultra-short wind speed prediction means predicting the wind speed at a single future sampling point) prediction is quite high. However, as the step size increases, the accuracy of the predictions diminishes significantly. To solve this problem, we propose a novel hybrid multi-step wind speed prediction system, which adopts a customized deep learning approach and a multi-strategy improved optimizer to achieve more accurate multi-step prediction results. In the high-precision prediction subsystem, we initially break down the data into five distinct components using Variational Mode Decomposition (VMD). At the same time, we establish a selection pool that consists of, ensemble learning, shallow neural network, and deep learning, and select the most appropriate prediction model for each component through a customized index. It is notable that we enhance the classical loss function by incorporating the Gaussian Kernel Function. In the intelligent weighted subsystem, we introduce an improved optimization algorithm, which adopts Halton Low-discrepancy Sequences for initialization and incorporates the concept of the JAYA algorithm to enhance the whale optimization algorithm (WOA), and ascertain the optimal weights for each component through the multi-strategy improved optimizer. Through five experiments on four sets of field data, we compare the proposed prediction system with twelve other classical models to prove the prediction accuracy. In three groups of discussions, we verify the improvement in global optimization ability of the multi-strategy improved optimizer through 15 single objective test functions, and explore its feasibility in practical engineering by recording the running time of the system.

Suggested Citation

  • Dong, Zhaochen & Tian, Zhirui & Lv, Shuang, 2025. "A novel paradigm for multi-step wind speed prediction: A hybrid system based on decomposition and weighted ensemble approach enhanced by Gaussian Kernel Function," Renewable Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:renene:v:253:y:2025:i:c:s0960148125011589
    DOI: 10.1016/j.renene.2025.123496
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    References listed on IDEAS

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