[1]蒋建东,张海峰,郭嘉琦.基于改进蜣螂优化算法的短期风电功率预测[J].郑州大学学报(工学版),2025,46(04):129-136.[doi:10.13705/j.issn.1671-6833.2025.01.015]
　JIANG Jiandong,ZHANG Haifeng,GUO Jiaqi.Short-term Wind Power Prediction Based on Improved Dung Beetle Optimization Algorithm[J].Journal of Zhengzhou University (Engineering Science),2025,46(04):129-136.[doi:10.13705/j.issn.1671-6833.2025.01.015]
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基于改进蜣螂优化算法的短期风电功率预测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年04期
页码:
129-136
栏目:
出版日期:
2025-07-10

文章信息/Info

Title:
Short-term Wind Power Prediction Based on Improved Dung Beetle Optimization Algorithm
文章编号:
1671-6833(2025)04-0129-08
作者:
蒋建东1;  张海峰1;  郭嘉琦2
1. 郑州大学 电气与信息工程学院,河南 郑州 450001;2. 国网河南省电力公司 安阳供电公司,河南 安阳 455000
Author(s):
JIANG Jiandong1;  ZHANG Haifeng1;  GUO Jiaqi2
1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. Anyang Power Supply Company, State Grid Henan Electric Power Company, Anyang 455000, China
关键词:
风电功率预测;  改进蜣螂优化算法;  变分模态分解;  卷积神经网络;  双向长短期记忆神经网络
Keywords:
wind power prediction;  improved dung beetle optimization algorithm;  VMD;  CNN;  BiLSTM
分类号:
TM614
DOI:
10.13705/j.issn.1671-6833.2025.01.015
文献标志码:
A
摘要:
为了提高短期风电功率预测的准确度,建立了一种基于 POTDBO-VMD-CNN-BiLSTM 的短期风电功率预测模型。 首先,采用融合 Piecewise 混沌映射、鱼鹰优化算法和自适应 T 分布扰动 3 种策略对蜣螂优化算法进行改进,以平衡蜣螂优化算法的全局探索和局部开发能力并加快其收敛速度;其次,用改进的蜣螂优化算法( POTDBO) 对变分模态分解(VMD)的分解数 K 和惩罚因子 α 进行寻优处理,提高 VMD 的分解效果,再用 POTDBO-VMD 模型对风电功率进行分解;最后,将分解的各频率分量以及残差分量分别输入到 CNN-BiLSTM 混合模型中预测,再将各频率分量以及残差分量的预测结果进行序列重构得到风电功率预测结果。 通过新疆某风电场和吉林某风电场的实际数据对所提出模型进行实验,并和 CNN-BiLSTM 模型进行对比,结果显示:所提模型在决定系数 R2 上分别增加了 4. 21%,7. 69%,表现出更好的预测精度。
Abstract:
A short-term wind power prediction model based on POTDBO-VMD-CNN-BiLSTM was proposed to improve the accuracy of short-term wind power prediction. Firstly, three strategies were adopted to improve the dung beetle optimization algorithm, including integrating Piecewise chaotic mapping, integrating Osprey optimization algorithm, and integrating adaptive T-distribution perturbation, in order to balance the global exploration and local development capabilities of the dung beetle optimization algorithm and accelerate its convergence speed. Secondly, the improved dung beetle optimization algorithm ( POTDBO) was used to optimize the decomposition number and penalty factor of variational mode decomposition (VMD) to improve the decomposition effect of VMD. Then, the POTDBO-VMD model was used to decompose the wind power. Finally, the decomposed frequency components and residual components were input into the CNN-BiLSTM hybrid model for prediction, and the prediction results of each frequency component and residual component were sequentially reconstructed to obtain the wind power prediction results. The proposed model was experimentally tested using actual data from wind farms in Xinjiang and Jilin. Compared with the CNN-BiLSTM model, the results showed that the proposed model increased by 4. 21% and 7. 69% on R 2 respectively, demonstrating better prediction accuracy.

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相似文献/References:

[1]孙晓燕,时良振,徐瑞东,等.基于区间样本和回声状态网络的风电功率不确定性预测[J].郑州大学学报(工学版),2017,38(01):56.[doi:10.13705/j.issn.1671-6833.2017.01.003]
　Sun Xiaoyan,Shi Liangzhen,Xu Ruidong,et al.Forecast of wind power generation with uncertainty based on interval sample and echo state network[J].Journal of Zhengzhou University (Engineering Science),2017,38(04):56.[doi:10.13705/j.issn.1671-6833.2017.01.003]

更新日期/Last Update: 2025-07-13