[1]张文煜,马可可,郭振海,等.基于灰狼算法和极限学习机的风速多步预测[J].郑州大学学报(工学版),2024,45(02):89-96.[doi:10.13705/j.issn.1671-6833.2026.05.008]
 ZHANG Wenyu,MA Keke,GUO Zhenhai,et al.Multistep Prediction of Wind Speed Based on Grey Wolf Algorithm and Extreme Learning Machine[J].Journal of Zhengzhou University (Engineering Science),2024,45(02):89-96.[doi:10.13705/j.issn.1671-6833.2026.05.008]
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基于灰狼算法和极限学习机的风速多步预测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45卷
期数:
2024年02期
页码:
89-96
栏目:
出版日期:
2024-03-06

文章信息/Info

Title:
Multistep Prediction of Wind Speed Based on Grey Wolf Algorithm and Extreme Learning Machine
作者:
张文煜 马可可 郭振海 赵 晶 邱文智
1. 郑州大学 地球科学与技术学院,河南 郑州 450001;2. 郑州大学 计算机与人工智能学院,河南 郑州 450001; 3. 中国科学院大气物理研究所 大气科学和地球流体力学数值模拟国家重点实验室,北京 100029
Author(s):
ZHANG Wenyu MA Keke GUO Zhenhai ZHAO Jing QIU Wenzhi
1. School of Earth Sciences and Technology, Zhengzhou University, Zhengzhou 450001, China; 2. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China; 3. State Key Laboratory of Numerical Modeling of Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
关键词:
风速预测 多步预测 信号分解 特征选择 灰狼优化算法 极限学习机
Keywords:
wind speed prediction multi-step prediction signal decomposition selection of features grey wolf optimization extreme learning machine
DOI:
10.13705/j.issn.1671-6833.2026.05.008
文献标志码:
A
摘要:
为了提高风速的多步预测水平,提出了一种基于数据信号分解和灰狼算法优化极限学习机的混合预测模 型。 首先,使用具有自适应噪声的完全集成经验模态分解算法将原始风速时间序列分解为若干本征模态函数和一 个残差序列,并使用偏自相关函数法对模型输入进行特征选择;其次,在分解子序列上分别建立模型并进行预测, 构造多输入多输出策略的极限学习机神经网络,使用灰狼优化算法求解其中的最优化隐含层权值和偏置;最后,对 子序列进行重构并得到最终的预测结果。 使用时间分辨率为 15 min 的多组实测资料开展模拟实验,所提模型在 3 个风电场的均方根误差分别为 0. 859、0. 925、0. 927 m / s,均低于其他对比模型,验证了该模型在未来 4 h 风速预 测即 16 步预测中的有效性。
Abstract:
In order to improve the multi-step prediction of wind speed, a hybrid prediction model based on data signal decomposition and grey wolf optimization algorithm was proposed to optimize extreme learning machine. Firstly, the original wind speed time series was decomposed into several intrinsic mode functions and a residual sequence using the complete ensemble empirical mode decomposition with adaptive noise, and the partial autocorrelation function model input. Then, the model was built and the prediction was made on the decomposition subsequence. An extreme learning machine neural network with multi-input-multi-output strategy was constructed, and grey wolf algorithm was used to solve the weight and bias of the optimal hidden layer. Finally, the subsequence was reconstructed and the final prediction result was obtained. Simulation experiments were conducted using multiple sets of measured data with a time resolution of 15 minutes. The root mean square errors of the proposed model in the three wind farms were 0. 859, 0. 925, and 0. 927, respectively, which were lower than other comparative models, verifying the effectiveness of the model in predicting wind speed in the next four hours,i. e. 16 steps prediction.
更新日期/Last Update: 2024-03-08