基于黑翅鸢算法和VMD的短期风电功率预测

周金涛; 何山; 王维庆; 程静; 房忠; 陈军

doi:doi:10.19912/j.0254-0096.tynxb.2024-1145

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基于黑翅鸢算法和VMD的短期风电功率预测

更新时间：2026-01-08

- 基于黑翅鸢算法和VMD的短期风电功率预测
- Vol. 46, Issue 12, Pages: 762-773(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1145
  CLC： TM614
- Published Online：07 January 2026，
  
  Published：2025
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周金涛, 何山, 王维庆, et al. 基于黑翅鸢算法和VMD的短期风电功率预测[J]. 2025, 46(12): 762-773.
DOI：

周金涛, 何山, 王维庆, et al. 基于黑翅鸢算法和VMD的短期风电功率预测[J]. 2025, 46(12): 762-773. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1145.

摘要

为提高短期风电功率预测精度

提出一种基于黑翅鸢算法（BKA）和变分模态分解（VMD）的短期风电功率预测模型。首先采用BKA确定VMD的参数

并且利用VMD将数据分解为若干个子序列以降低风电时间序列的复杂性和不稳定性。其次将各子序列与关键的气象数据结合构成输入分量

并利用时间卷积网络（TCN）、双向门控循环单元（BiGRU）和注意力机制的组合预测模型对各输入分量分别进行预测。最后采用BKA确定预测模型超参数的最优组合方案

并将各预测结果进行加和重构得到最终风电功率预测结果。采用新疆某风电厂的实际风电功率数据对所提模型进行验证

并与其他5种组合预测模型进行性能对比。实验表明

所提模型预测效果优于其他预测模型

能够有效提升短期风电功率预测的准确性。

Abstract

To improve short-term wind power forecasting accuracy

a predictive model based on the Black Kite Algorithm （BKA） and Variational Mode Decomposition （VMD） was been proposed. Initially

the BKA algorithm was used to determine the VMD parameters

allowing VMD to decompose the data into multiple subsequences

which helps reduce the complexity and volatility of wind power time series. Each subsequence was then combined with essential meteorological data

creating input components for the predictive model

which is a hybrid framework involving a Temporal Convolutional Network （TCN）

Bidirectional Gated Recurrent Units （BiGRU）

and an attention mechanism. This composite model independently forecasts each input component

after which BKA optimizes the model’s hyperparameters to obtain the best combination scheme. The final wind power forecast was produced by summing and reconstructing the individual predictions. This model was validated using real wind power data from a wind farm in Xinjiang

and its performance was compared with five other combined forecasting models. Experimental results indicate that the prediction effect of the proposed model is superior to that of other predicition models

effectively enhancing the accuracy of short-term wind power forecasting.

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