[1]贾世会,刘立夫,迟晓妮,等.基于BWO和WOA的VMD-LSTM短期风速预测[J].郑州大学学报(工学版),2025,46(03):59-66.[doi:10.13705/j.issn.1671-6833.2024.06.014]
　JIA Shihui,LIU Lifu,CHI Xiaoni,et al.VMD-LSTM Short-term Wind Speed Prediction Model Based on BWO and WOA[J].Journal of Zhengzhou University (Engineering Science),2025,46(03):59-66.[doi:10.13705/j.issn.1671-6833.2024.06.014]
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基于BWO和WOA的VMD-LSTM短期风速预测()
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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03期
页码:
59-66
栏目:
出版日期:
2025-05-13

文章信息/Info

Title:
VMD-LSTM Short-term Wind Speed Prediction Model Based on BWO and WOA
文章编号:
1671-6833(2025)03-0059-08
作者:
贾世会1; 2;  刘立夫1;  迟晓妮2; 3;  李高西4
1.武汉科技大学 理学院, 湖北 武汉 430081;2.武汉科技大学 冶金工业过程系统科学湖北省重点实验室, 湖北 武汉 430081;3.桂林电子科技大学 数学与计算机科学学院, 广西 桂林 541004;4.重庆工商大学 数学与统计学院,重庆 400067
Author(s):
JIA Shihui1; 2 ;  LIU Lifu1;  CHI Xiaoni2; 3;  LI Gaoxi4
1.College of Science,Wuhan University of Science and Technology, Wuhan 430081, China; 2.Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430081, China; 3.School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China; 4.School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067,China
关键词:
白鲸优化算法;  鲸鱼优化算法;  变分模态分解;  LSTM;  风速预测
Keywords:
beluga whale optimization;  whale optimization algorithm;  variational mode decomposition;  LSTM;  wind speed prediction
分类号:
TP391
DOI:
10.13705/j.issn.1671-6833.2024.06.014
文献标志码:
A
摘要:
针对风电机组组网运行存在的功率波动性和随机性,为提高风速预测的精度和风电机组运行的稳定性,提出了一种基于白鲸优化算法和鲸鱼优化算法的VMD-LSTM短期风速预测模型。首先,利用白鲸优化算法对VMD中的模态数及惩罚因子进行优化,得到分解的子序列;其次,对于LSTM中的隐含层节点数、最大训练次数和初始学习率等参数,使用鲸鱼优化算法进行确定;最后,利用LSTM的非线性拟合能力对数据进行预测。结果表明:所提预测模型在测试集上的RMSE、MAE、MAPE分别为0.223 4,0.172 7,0.083 7,均低于其他对比模型,验证了所提模型在短期风速预测问题上的有效性。
Abstract:
In view of the power fluctuation and randomness existing in the operation of wind turbine networks, to improve the accuracy of wind speed prediction and the stability of wind turbine operation, in this study a VMDLSTM short-term wind speed prediction model was proposed based on the beluga whale optimization and the whale optimization algorithm. Firstly, the Beluga optimization algorithm was used to optimize the number of modes and penalty factors in VMD to obtain the reorganized subsequence. For parameters such as the number of hidden layer nodes, the maximum number of training generations, and the initial learning rate in LSTM, the whale optimization algorithm was used to determine these parameters. Finally, the monomer transplantation ability of LSTM was utilized to predict the data. The results indicated that the VMD-LSTM prediction model based on BWO and WOA proposed in this study achieved RMSE, MAE, and MAPE values of 0.223 4, 0.172 7, and 0.083 7, respectively, on the test set, all of which were lower than those of other comparative models. This validated the effectiveness of the proposed model in short-term wind speed prediction.

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更新日期/Last Update: 2025-05-22