[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]
点击复制

基于改进蜣螂算法的短期风电功率预测()
分享到：

《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:
2024-11-30

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

参考文献/References:

[1] 韩自奋,景乾明,张彦凯,等.风电预测方法与新趋势综述[J].电力系统保护与控制, 2019, 47(24):178-187.

[2] Gong M J, Yan C C, Xu W, et al. Short-term wind power forecasting model based on temporal convolutional network and Informer[J]. Energy, 2023, 283.

[3] 涂思嘉,杨悦荣,林舜江,等. 考虑风电不确定性的交直流混联电网静态电压稳定优化控制方法[J]. 电力科学与技术学报, 2023, 38(3): 94-104.

[4] 张颖超,成金杰,邓华,等.基于相似日和特征提取的短期风电功率预测[J].郑州大学学报(工学版),2020,41(05):44-49.

[5] 蒋建东,孙书凯,董存,等.风电中长期电量预测研究现状[J].高电压技术, 2022, 48(02):409-419.

[6] Antonanzas J, Osorio N, Escobar R, et al. Review of photovoltaic power forecasting [J]. Solar Energy 2016; 136: 78–111 .

[7] Ahmed R, Sreeram V, Mishra Y, et al. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization [J]. Renewable and Sustainable Energy Reviews, 2020, 124: 1-26.

[8] Soubdhan T, Ndong J, Ould Baba et al. A robust forecasting framework based on the kalman filtering approach with a twofold parameter tuning procedure: Application to solar and photovoltaic prediction [J]. Solar Energy 2016, 131: 246-259 .

[9] DE ALENCAR D B, DE MATTOS AFFONSO C, DE OLIVEIRA R C L, et al. Different models for forecasting wind power generation: case study[J]. Energies, 2017, 12(10): 1-27 .

[10] Yang D. Making reference solar forecasts with climatology, persistence, and their optimal convex combination [J]. Solar Energy 2019, 193: 981-985.

[11] Hu W, Yang C. Grey model of direct solar radiation intensity on the horizontal plane for cooling loads calculation plane for cooling loads calculation [J]. Building and Environment 2000, 35: 587–593.

[12] Wang S, Li B, Li G Z, et al. Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration[J]. Applied Energy, 2021, 292.

[13] Liu W, Liu Y M, Fu L, et al. Wind Power Forecasting Method Based on Bidirectional Long Short-Term Memory Neural Network and Error Correction[J].Electric Power Components and Systems, 2022, 49(13-14):1169-1180.

[14] Li Z, Xu R S, Luo X R, et al. Short-term wind power prediction based on modal reconstruction and CNN-BiLSTM[J]. Energy Reports, 2023, 96449-6460.

[15] 李润金,李丽霞.基于VMD-CNN-LSTM模型的短期风电功率预测[J].沈阳工程学院学报(自然科学版), 2024, 20(01):6-13.

[16] 陈申,叶小岭,熊雄等.基于天鹰优化算法的短期风电功率区间预测[J].重庆理工大学学报(自然科学), 2023, 37(04):304-314.

[17] 欧阳资生,唐伯聪.基于VMD-Bi LSTM-ATT预测模型的碳中和指数量化投资研究[J].金融经济,2023(10):75-90.

[18] 肖烈禧,张玉,周辉,等基于IAOA-VMD-LSTM的超短期风电功率预测[J].太阳能学报, 2023, 44(11):239-246.

[19] 李飞宏,肖迎群.基于VMD-GRU-EC的短期电力负荷预测方法[J].中国测试,2023,49(10):120-127.

[20] XUE J, SHEN B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J/OL]. The Journal of Sup ercomputing, 2023, 79(7): 7305-7336.

[21] 郭琴,郑巧仙.多策略改进的蜣螂优化算法及其应用[J/OL].计算机科学与探索:1-22[2023-12-27].

[22] Dehghani Mohammad, Trojovský Pavel. Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems[J]. Frontiers in Mechanical Engineering, 2023,8：1126450.

[23] 李津,史加荣,张琰妮,等.基于最大信息系数的短期太阳辐射协同估计[J].太阳能学报, 2023, 44(09):286-294.

[24] 杨锡运,刘玉奇,李建林.基于四分位法的含储能光伏电站可靠性置信区间计算方法[J].电工技术学报, 2017,32(15):136-144.

[25] 杨子民,彭小圣,熊予涵等.计及邻近风电场信息与CNN-BiLSTM的短期风电功率预测[J].南方电网技术, 2023, 17(02):47-56.

[26] 辛征,王琦,刘兴然.短期风电功率预测的深度学习模型[J].计算机时代, 2023(02):33-36+41.

[27] 赵志浩.面向手机部件的目标区域检测算法的设计与实现[D]．沈阳：中国科学院大学(中国科学院沈阳计算技术研究所)，2020.

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(pre):56.[doi:10.13705/j.issn.1671-6833.2017.01.003]

更新日期/Last Update: 2024-10-10