风速重构聚类的元启发双向记忆预测方法

史晓航; 潘超; 王超; 李载源

doi:10.19725/j.cnki.1007-2322.2023.0381

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风速重构聚类的元启发双向记忆预测方法

新能源电力系统 | 更新时间：2026-02-02

- 风速重构聚类的元启发双向记忆预测方法
- Meta-inspired Bidirectional Memory Prediction Method for Wind Speed Reconstruction Clustering
- 现代电力 2026年43卷第1期页码：1-9
- 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：
  
  [ "史晓航(2000)，女，硕士研究生，通信作者，研究方向为新能源发电并网，E-mail：728560908@qq.com" ]
  [ "潘超(1981)，男，博士，教授，研究方向为电力系统稳定与电磁兼容研究工作，E-mail：31563018@qq.com" ]
  [ "王超(1999)，男，硕士研究生，研究方向为新能源发电并网，E-mail：2387618195@qq.com" ]
  [ "李载源(2000)，男，硕士研究生，研究方向为新能源发电并网，E-mail：2634803557@qq.com" ]
- 基金信息：
  
  国家重点研发计划项目(2022YFB2404000)。
- DOI：10.19725/j.cnki.1007-2322.2023.0381
  中图分类号： 1
- 纸质出版：2026
- 稿件说明：
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史晓航, 潘超, 王超, 等. 风速重构聚类的元启发双向记忆预测方法[J]. 现代电力, 2026,43(1):1-9.

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.
史晓航, 潘超, 王超, 等. 风速重构聚类的元启发双向记忆预测方法[J]. 现代电力, 2026,43(1):1-9. DOI： 10.19725/j.cnki.1007-2322.2023.0381.

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.

关键词

Keywords

references

ZHANG Bo, SHEN Jianjian, CHENG Chuntian, et al . Peak-shaving method of hydro-wind power complementary system based on C-vine Copula theory [J ] . Proceedings of the CSEE, 2022, 42(15): 5523−5534(in Chinese).

张博, 申建建, 程春田, 等. 基于C藤Copula理论的水风互补系统调峰方法[J]. 中国电机工程学报, 2022, 42(15): 5523−5534.

ZHANG Xu, ZHANG Hongli, WANG Cong. Wind speed prediction model based on PSOGSA full-parameter continuous fractions[J]. Power System Protection and Control, 2020, 48(23): 100−107(in Chinese).

张旭, 张宏立, 王聪. 基于PSOGSA全参数连分式的风速预测模型[J]. 电力系统保护与控制, 2020, 48(23): 100−107.

WANG Bo, FENG Shuanglei, LIU Chun. Uncertainty evaluation of wind power prediction considering impacts of power curve and predicted wind speed[J]. Power System Technology, 2014, 38(2): 463−468(in Chinese).

王勃, 冯双磊, 刘纯. 考虑预报风速与功率曲线因素的风电功率预测不确定性估计[J]. 电网技术, 2014, 38(2): 463−468.

GUAN Yongfeng, YU Min, HU Jia. Ultra-short-term wind speed prediction based on an adaptive integrated model[J]. Power System Protection and Control, 2022, 50(4): 120−128(in Chinese).

关永锋, 喻敏, 胡佳. 基于自适应组合模型的超短期风速预测[J]. 电力系统保护与控制, 2022, 50(4): 120−128.

SU Yun, BU Fanpeng, GUO Naiwang, et al . Research on short-term load forecast using multi-task with low-rank representation[J ] . Modern Electric Power, 2019, 36(3): 58−65(in Chinese).

苏运, 卜凡鹏, 郭乃网, 等. 基于低秩表示的多任务短期电力负荷预测的研究[J]. 现代电力, 2019, 36(3): 58−65.

LIU Hui, MI Xiwei, LI Yanfei, et al . Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short-term memory neural network and Elman neural network[J ] . Energy Conversion and Management, 2018(156): 498−514(in Chinese).

YU Xijuan, LI Xin, XUAN Zhenwen, et al . Fault cause identification method of distribution network based on empirical mode decomposition and long-short term memory network[J ] . Modern Electric Power, 2023, 40(4): 596−604(in Chinese).

于希娟, 李欣, 宣振文, 等. 基于经验模态分解与长短时记忆网络的配电网故障原因识别方法[J]. 现代电力, 2023, 40(4): 596−604.

YAO Yue, LIU Da. Short-term wind power forecasting based on attention mechanism of CNN-LSTM[J]. Modern Electric Power, 2022, 39(2): 212−218(in Chinese).

姚越, 刘达. 基于注意力机制的卷积神经网络-长短期记忆网络的短期风电功率预测[J]. 现代电力, 2022, 39(2): 212−218.

CHEN Jianting, XIANG Yang. Survey of unstable gradients in deep neural network training[J]. Journal of Software, 2018, 29(7): 2071−2091(in Chinese).

陈建廷, 向阳. 深度神经网络训练中梯度不稳定现象研究综述[J]. 软件学报, 2018, 29(7): 2071−2091.

CHEN Chenpeng, ZHAO Xin, BI Guihong, et al . SSA-Res-GRU short-term wind speed prediction model based on multi-model decomposition[J ] . Power System Technology, 2022, 46(8): 2975−2985(in Chinese).

陈臣鹏, 赵鑫, 毕贵红, 等. 基于多模式分解和麻雀优化残差网络的短期风速预测模型[J]. 电网技术, 2022, 46(8): 2975−2985.

HAO Huan, WANG Huali, WEI Qin. Theory of empirical mode decomposition and its application[J]. Chinese High Technology Letters, 2016, 26(1): 67−80(in Chinese).

郝欢, 王华力, 魏勤. 经验模态分解理论及其应用[J]. 高技术通讯, 2016, 26(1): 67−80.

ZHAO Xin, CHANG Xiqiang, LI Mengnü. Hybrid multi-step decomposition short-term wind speed prediction method based on ICEEMDAN-PE/FE-IGWO-SVR[J]. Modern Electronics Technique, 2023, 46(1): 124−130(in Chinese).

赵鑫, 常喜强, 李梦女. 基于ICEEMDAN-PE/FE-IGWO-SVR的混合多步分解短期风速预测方法[J]. 现代电子技术, 2023, 46(1): 124−130.

CI Song, LIU Qianwei, KANG Chongqing, et al . Fundamental exploration into ICT-energy fusion[J ] . Proceedings of the CSEE, 2021, 41(7): 2289−2297(in Chinese).

慈松, 刘前卫, 康重庆, 等. 从“信息–能量”基本关系看信息能源深度融合[J]. 中国电机工程学报, 2021, 41(7): 2289−2297.

PAN Chao, LI Runyu, CAI Guowei, et al . Combined forecasting model considering wind speed attribute reduction and clustering[J ] . Power System Technology, 2022, 46(4): 1355−1362(in Chinese).

潘超, 李润宇, 蔡国伟, 等. 考虑风速属性约简聚类的组合预测模型[J]. 电网技术, 2022, 46(4): 1355−1362.

WU Yaxiong, GAO Chong, CAO Huazhen, et al . Clustering analysis of daily load curves based on gwo algorithm[J ] . Power System Protection and Control, 2020, 48(6): 68-76(in Chinese).

吴亚雄, 高崇, 曹华珍, 等. 基于灰狼优化聚类算法的日负荷曲线聚类分析[J]. 电力系统保护与控制, 2020, 48(6): 68-76.

XU Qing shan, LOU Oudie, ZHENG Aixia, et al . A non-intrusive load decomposition method based on affinity propagation and genetic algorithm optimization[J ] . Transactions of China Electrotechnical Society, 2018, 33(16): 3868−3878(in Chinese).

徐青山, 娄藕蝶, 郑爱霞, 等. 基于近邻传播聚类和遗传优化的非侵入式负荷分解方法[J]. 电工技术学报, 2018, 33(16): 3868−3878.

JI Xinge, LI Hui, LIU Sijia, et al . Short-term photovoltaic power forecasting based on MIE-LSTM[J ] . Power System Protection and Control, 2020, 48(7): 50−57(in Chinese).

吉锌格, 李慧, 刘思嘉, 等. 基于MIE-LSTM的短期光伏功率预测[J]. 电力系统保护与控制, 2020, 48(7): 50−57.

ZHANG Yujiao, HUANG Rui, ZHANG Fuquan, et al . Study on affinity propagation clustering algorithm based on bacterial flora optimization[J ] . Computer Science, 2022, 49(5): 165−169(in Chinese).

张宇姣, 黄锐, 张福泉, 等. 基于菌群优化的近邻传播聚类算法研究[J]. 计算机科学, 2022, 49(5): 165−169.

ZHOU Chujie. A novel hybrid deep learning model for short-term wind speed forecasting based on LSTM and TCN[D]. Lanzhou: Lanzhou University, 2019(in Chinese).

周楚杰. 基于LSTM和TCN混合深度学习的风速短期预测模型[D]. 兰州: 兰州大学, 2019.

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