首站-论文投稿智能助手
典型文献
Data Completion for Power Load Analysis Considering the Low-rank Property
文献摘要:
With large-scale applications,the loss of power load data during transmission is inevitable.This paper proposes a data completion method considering the low rank property of the data.According to the low-rank property of data and numerical experiments,we find either the linear interpolation(LI)or the singular value decomposition(SVD)based method is superior to other methods depending on the smoothness of the data.We construct an index to measure the smoothness of data,and propose the SVDLI algorithm which adaptively selects different algorithms for data completion according to the index.Numerical simulations show that irrespective of the smoothness of data,the data complementing results of SVDLI are comparable to or better than the best of SVD or LI algorithms.The present study is verified using the measurements in China,and the public data of the Australian electricity distribution company and Lawrence Berkeley National Laboratory.
文献关键词:
作者姓名:
Chijie Zhuang;Jianwei An;Zhaoqiang Liu;Rong Zeng
作者机构:
Department of Elec-trical engineering,Tsinghua University,Beijing 100084,China;School of Computing,National University of Singapore,Singapore 117417,Singapore
引用格式:
[1]Chijie Zhuang;Jianwei An;Zhaoqiang Liu;Rong Zeng-.Data Completion for Power Load Analysis Considering the Low-rank Property)[J].中国电机工程学会电力与能源系统学报(英文版),2022(06):1751-1759
A类:
SVDLI
B类:
Data,Completion,Power,Load,Analysis,Considering,Low,rank,Property,With,large,scale,applications,loss,power,load,data,during,transmission,inevitable,This,paper,proposes,completion,considering,low,property,According,numerical,experiments,find,either,linear,interpolation,singular,value,decomposition,superior,other,methods,depending,smoothness,We,construct,which,adaptively,selects,different,algorithms,according,Numerical,simulations,show,that,irrespective,complementing,results,are,comparable,better,than,best,present,study,verified,using,measurements,China,public,Australian,electricity,distribution,company,Lawrence,Berkeley,National,Laboratory
AB值:
0.588406
相似文献
106 New Emission-line Galaxies and 29 New Galactic H Ⅱ Regions are Identified with Spectra in the Unknown Data Set of LAMOST DR7
Yan Lu;A-Li Luo;Li-Li Wang;You-Fen Wang;Yin-Bi Li;Jin-Shu Han;Li Qin;Yan-Ke Tang;Bo Qiu;Shuo Zhang;Jian-Nan Zhang;Yong-Heng Zhao-CAS Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China;College of Computer and Information Engineering&Institute for Astronomical Science,Dezhou University,Dezhou 253023,China;University of Chinese Academy of Science,Beijing 100049,China;College of Physics and Electronic Information,Dezhou University,Dezhou 253023,China;School of Electronic Information Engineering,Hebei University of Technology,Tianjin 300401,China;Department of Astronomy,School of Physics,Peking University,Beijing 100871,China;Kavli institute of Astronomy and Astrophysics,Peking University,Beijing 100871,China
The Electromagnetic Characteristics of the Tianlai Cylindrical Pathfinder Array
Shijie Sun;Jixia Li;Fengquan Wu;Peter Timbie;Reza Ansari;Jingchao Geng;Huli Shi;Albert Stebbins;Yougang Wang;Juyong Zhang;Xuelei Chen-National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;Department of Physics,University of Wisconsin Madison,1150 University Ave,Madison WI 53703,United States of America;IJC Lab,CNRS/IN2P3&UniversitéParis-Saclay,15 rue Georges Clémenceau,F-91405 Orsay,France;The 54th Research Institute,China Electronics Technology Group Corporation,Shijiazhuang,Hebei 050051,China;Fermi National Accelerator Laboratory,P.O.Box 500,Batavia IL 60510-5011,United States of America;School of Mechanical Engineering,Hangzhou Dianzi University,Hangzhou 310017,China;Department of Physics,College of Sciences,Northeastern University,Shenyang 110819,China;Center of High Energy Physics,Peking University,Beijing 100871,China
机标中图分类号,由域田数据科技根据网络公开资料自动分析生成,仅供学习研究参考。