摘要
风电功率预测(wind power prediction,WPP)技术是电力系统调度与安全运行的关键性因素,为了更好地提升风电功率预测技术的精度,在集成学习的基础上提出了一种多重集成的集群短期WPP方法。所提方法包含4步:第1步,利用变分模式分解、经验模态分解和小波变换将原始风电序列分解为多个子序列;第2步,根据子序列构造多个堆叠去噪自动编码器(stacked denoising autoencoders,SDAE)进行深度学习;第3步,将第2步的结果随机划分成几个集合,利用支持向量机(support vector machine,SVM)对每个集合进行集成;第4步,将第3步的集成的结果再随机划分成几个集合,利用SVM对每个集合进行集成,重复以上步骤直至得到最终的集成预测结果。结果表明,多重集成学习得到前96 h预测结果的平均归一化均方根误差相比单次集成减少了0.0101,百分比为9.01%;相比SDAE减少了0.0151,百分比为13.54%;相比SVM减少了0.0175,百分比为14.66%。论文研究可为基于深度学习和集成学习的风电集群短期功率预测提供参考。
Wind power prediction(WPP)technology is a key factor in power system scheduling and safe operation.In order to better improve the accuracy of the WPP technology,this paper proposes a multi-integrated cluster short-term WPP method based on ensemble learning,which includes four steps.The first step is to decompose the original wind power sequence into multiple sub-sequences by using variational mode decomposition,empirical mode decomposition and wavelet transform.The second step is to construct multiple stacked denoising autoencoders(SDAEs)based on subsequences for deep learning.The third step is to randomly divide the results of the second step into several sets,and integrate each set using the support vector machine(SVM).The fourth step is to randomly divide the integration results of the third step into several sets,integrate each set with SVM,and repeat the above steps until the final integration prediction result is obtained.The results show that the average normalized root mean square error(RMSE)of the first 96 h prediction results obtained by multiple integration learning is reduced by 0.0101(9.01%)compared with that obtained by single integration learning.Compared with SDAE,it decreases by 0.0151,and the percentage is 13.54%.Compared with SVM,it decreases by 0.0175,a percentage of 14.66%.This paper can provide reference for short term power prediction of wind power cluster based on deep learning and ensemble learning.
作者
李聪
彭小圣
王皓怀
车建峰
王勃
刘纯
LI Cong;PENG Xiaoseng;WANG Haohuai;CHE Jianfeng;WANG Bo;LIU Chun(State Key Laboratory of Strong Electromagnetic Engineering and New Technology,School of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;Power Dispatching Control Center of China Southern Power Grid,Guangzhou 510623,China;State Key Laboratory of New Energy and Storage Operation Control,China Electric Power Research Institute,Beijing 100192,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2022年第2期504-512,共9页
High Voltage Engineering
基金
国家重点研发计划(促进可再生能源消纳的风电/光伏发电功率预测技术及应用)(2018YFB0904200)
中国南方电网有限责任公司科技项目(0000002020030103XT00015)。
关键词
信号分解技术
深度学习
集成学习
风电
短期预测
风电集群功率预测
signal decomposition technology
deep learning
ensemble learning
wind power
short-term forecast
wind farm cluster power prediction
