Meta-inspired Bidirectional Memory Prediction Method for Wind Speed Reconstruction Clustering

史晓航; 潘超; 王超; 李载源

doi:10.19725/j.cnki.1007-2322.2023.0381

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Meta-inspired Bidirectional Memory Prediction Method for Wind Speed Reconstruction Clustering

更新时间：2026-02-02

- Meta-inspired Bidirectional Memory Prediction Method for Wind Speed Reconstruction Clustering
- Vol. 43, Issue 1, Pages: 1-9(2026)
- 作者机构：
  
  电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林市,吉林省,132012
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0381
  CLC： 1
- Published：2026
- 稿件说明：
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史晓航, 潘超, 王超, et al. Meta-inspired Bidirectional Memory Prediction Method for Wind Speed Reconstruction Clustering[J]. 2026, 43(1): 1-9.
DOI：

史晓航, 潘超, 王超, et al. Meta-inspired Bidirectional Memory Prediction Method for Wind Speed Reconstruction Clustering[J]. 2026, 43(1): 1-9. DOI： 10.19725/j.cnki.1007-2322.2023.0381.

摘要

风速的准确预测对于规模化风电并网及安全运行非常关键。该文首先采用完全自适应噪声集合经验模态分解法将风速序列分解为若干模态分量，结合快速相关滤波，实现模态分量的优选与降维，重构样本集合。其次，选用高斯核距离度量样本间距，并优选初值，以改进K-medoids聚类，提升高维样本空间的聚类准确性和稳定性。在双向长短时记忆网络中嵌入元启发优化模块，构建元启发双向记忆网络。然后，输入训练样本寻优内置参数以及典型集测试样本寻优结构参数。最后，输出风速预测值。以东北地区某风场为研究对象进行算例仿真，验证预测模型的准确性和泛化能力。

Abstract

The accurate prediction of wind speed is important for the integration of large-scale wind power and its safe operation. The complementary ensemble empirical mode decomposition with adaptive noise is employed to decompose the wind speed sequence into several modal components

while the fast correlation filtering is utilized to optimize these modal components and reduce the dimensionality to reconstruct the sample set. The Gaussian kernel distance is utilized to measure the sample spacing

and an initial value is selected to improve the k-medoids clustering

so as to improve both the clustering accuracy and stability of the high-dimensional sample space. The meta-heuristic optimization module is embedded into the bidirectional long short-term memory network to construct the meta-heuristic bidirectional memory network. The typical set training samples are input to optimize the built-in parameters

while the typical set test samples are input to optimize the structural parameters. Finally

the wind speed prediction value is generated. The wind field in Northeast China is taken as the research object

and the accuracy and generalization ability of the prediction model are verified.

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references

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