四川轻化工大学学报（自然科学版）2024，Vol.37Issue(4)：68-74,7.DOI:10.11863/j.suse.2024.04.08
基于CEEMD-CNN-GRU的风电功率预测
Wind Power Prediction Based on CEEMD-CNN-GRU
摘要
Abstract
The traditional prediction model in wind power prediction is difficult to fully extract the spatio-temporal characteristics and hidden features in the wind farm historical data,and the prediction accuracy is low.Aiming at this problem,a CEEMD-CNN-GRU wind power prediction model has been proposed.Firstly,the power sequences are decomposed using complementary ensemble empirical modal decomposition(CEEMD)for different wind power scenarios to reduce the volatility of the wind power sequences.Then,the spatial features are extracted using the convolutional neural network(CNN),and the temporal features are extracted using the gated recurrent unit(GRU).Lastly,the wind power prediction is achieved,and the decomposed sequences are superposed to obtain the final prediction results.The results show that the designed model is highly accurate,and the root mean square error is reduced by 80.17%,77.07%and 71.07%compared to the CNN,GRU and CNN-GRU models,respectively.Moreover,the root mean square error is reduced by 78.63%and 66.61%after turbines are grouped and scenarios are divided compared with those without turbines grouping or scenarios dividing,respectively.关键词
短期风电功率预测/互补集合经验模态分解/卷积神经网络/门控循环单元Key words
short-term wind power prediction/complementary ensemble empirical mode decomposition/convolutional neural networks/gated recurrent unit分类
信息技术与安全科学引用本文复制引用
赵敏,王孟军,刁海岸,黄凯峰..基于CEEMD-CNN-GRU的风电功率预测[J].四川轻化工大学学报（自然科学版）,2024,37(4):68-74,7.基金项目
国家重点实验室开放基金项目(SKLMRDPC21KF23) （SKLMRDPC21KF23）
安徽省高校优秀青年人才支持计划项目(gxyq2022068) （gxyq2022068）
校级重点教育教学改革研究项目(2023hsjyxm24) （2023hsjyxm24）
