郑州大学学报（工学版）2024，Vol.45Issue(2)：89-96,8.DOI:10.13705/j.issn.1671-6833.2023.05.008
基于灰狼算法和极限学习机的风速多步预测
Multistep Prediction of Wind Speed Based on Grey Wolf Algorithm and Extreme Learning Machine
摘要
Abstract
In order to improve the multi-step prediction of wind speed,a hybrid prediction model based on data sig-nal 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 recon-structed 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,verif-ying the effectiveness of the model in predicting wind speed in the next four hours,i.e.16 steps prediction.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Key words
wind speed prediction/multi-step prediction/signal decomposition/selection of features/grey wolf op-timization/extreme learning machine分类
信息技术与安全科学引用本文复制引用
张文煜,马可可,郭振海,赵晶,邱文智..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(2):89-96,8.基金项目
国家自然科学基金资助项目(41875085) （41875085）
