[1]蒋建东,张海峰,郭嘉琦.基于改进蜣螂算法的短期风电功率预测[J].郑州大学学报(工学版),2024,45(pre):2.[doi:10. 13705 / j. issn. 1671-6833. 2025. 01. 015]
　JIANG Jiandong,ZHANG HaifenfGUO jiaqi.Short Term Wind Power Forecasting Based on Improved Dung Beetle Optimization Algorithm[J].Journal of Zhengzhou University (Engineering Science),2024,45(pre):2.[doi:10. 13705 / j. issn. 1671-6833. 2025. 01. 015]
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基于改进蜣螂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:
45
期数:
2024年pre
页码:
2
栏目:
出版日期:
2024-12-31

文章信息/Info

Title:
Short Term Wind Power Forecasting Based on Improved Dung Beetle Optimization Algorithm
作者:
蒋建东1; 张海峰1; 2; 郭嘉琦2
(郑州大学 电气与信息工程学院,河南 郑州450001)
Author(s):
JIANG Jiandong1;  ZHANG Haifenf1; 2GUO jiaqi2
(School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China)
关键词:
风电功率预测; 改进的蜣螂优化算法; 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 forecasting;  improved dung beetle optimization algorithm;  variational mode decomposition (VMD);  convolutional neural networks (CNN);  bidirectional long short term memory neural network ( 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分布扰动三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）的分解数目K和惩罚因子进行寻优处理，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。通过新疆和吉林某风电场的实际数据对所提出模型进行实验，并于CNN-BiLSTM模型进行对比，结果显示，本文模型在决定系数R2上分别增加了4.21%、7.14%，表现出更好的预测精度。
Abstract:
A short-term wind power prediction model based on POTDBO-VMD-CNN- BiLSTM is proposed in the thesis to improve the accuracy of short-term wind power prediction. Firstly, three strategies are 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) is 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 is used to decompose the wind power . Finally, the decomposed frequency components and residual components are input into the CNN-BiLSTM hybrid model for prediction, and the prediction results of each frequency component and residual component are sequentially reconstructed to obtain the wind power prediction results. The proposed model is experimentally tested using actual data from wind farm s in Xinjiang and Jilin . Compared with the CNN-BiLSTM model , the results show that the model in this thesis increases by 4.21% and 7.14% on R 2 respectively, demonstrating better prediction accuracy demonstrates better prediction accuracy

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[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]
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更新日期/Last Update: 2024-10-10