现代电子技术2025，Vol.48Issue(2)：97-102,6.DOI:10.16652/j.issn.1004-373x.2025.02.016
基于CEEMDAN-TCN的短期风电功率预测研究
Research on short-term wind power forecasting based on CEEMDAN-TCN
李敖 1冉华军 1李林蔚 1王新权 1高越1
作者信息
- 1. 三峡大学 电气与新能源学院,湖北 宜昌 443002
- 折叠
摘要
Abstract
Wind power generation,as an important component of renewable energy,plays a crucial role in power system planning and daily operation.Therefore,accurate short-term wind power forecasting is crucial for the stable operation and optimized scheduling of electrical grids.In order to enhance the precision of short-term wind power forecasting,a method of short-term wind power forecasting based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and temporal convolutional networks(TCN)is proposed.The CEEMDAN is used to decompose the initial wind power data,so as to obtain multiple several relatively stable sub-data sequences.The sub-data sequences are used as inputs for TCN,and the TCN model is used to conduct the feature extraction and power forecasting.All predicted values are aggregated to obtain the final power prediction value.The proposed method is verified by the real wind power data from a certain region in Ningxia,and compared with traditional prediction models.The results indicate that the proposed method has high prediction accuracy and can provide relevant references for short-term wind power forecasting and other related work.关键词
短期风电功率预测/自适应噪声的完备集合经验模态分解(CEEMDAN)/时间卷积网络(TCN)/特征提取/预测精度/时间序列分析Key words
short-term wind power forecasting/complete ensemble empirical mode decomposition with adaptive noise/temporal convolutional network/feature extraction/prediction accuracy/time series analysis分类
电子信息工程引用本文复制引用
李敖,冉华军,李林蔚,王新权,高越..基于CEEMDAN-TCN的短期风电功率预测研究[J].现代电子技术,2025,48(2):97-102,6.
