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

作　　者：李敖 冉华军[1] 李林蔚 王新权 高越 LI Ao;RAN Huajun;LI Linwei;WANG Xinquan;GAO Yue(College of Electrical Engineering and New Energy,China Three University,Yichang 443002,China)

机构地区：[1]三峡大学电气与新能源学院,湖北宜昌443002

出　　处：《现代电子技术》2025年第2期97-102,共6页Modern Electronics Technique

摘　　要：风力发电作为可再生能源的重要组成部分,在电力系统规划和日常运行中扮演着重要的角色,准确的短期风电功率预测对于电网的稳定运行和优化调度具有重要意义。为提高短期风电功率预测的准确性,提出一种基于自适应噪声完备集合经验模态分解和时间卷积网络的短期风电功率预测方法。首先利用自适应噪声完备集合经验模态分解对初始风电功率数据进行分解,得到多个相对稳定的子数据序列;然后将其分别作为时间卷积网络的输入,利用时间卷积网络模型进行特征提取和功率预测;最后将所有预测值进行汇总,得到最终的功率预测值。使用宁夏某地区真实风电功率数据进行验证,并与传统预测模型比较,结果表明所提方法具有较高的预测精度,可为风电功率短期预测等相关工作提供相关参考。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 

分 类 号：TN911.23-34[电子电信—通信与信息系统]