Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (2): 128-133.doi: 10.11896/jsjkx.191200152

• Database & Big Data & Data Science • Previous Articles     Next Articles

Power Load Data Completion Based on Sparse Representation

LI Pei-guan1, YU Zhi-yong1,2, HUANG Fang-wan1,2   

  1. 1 College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China
    2 Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou University,Fuzhou 350116,China
  • Received:2019-12-25 Revised:2020-04-16 Online:2021-02-15 Published:2021-02-04
  • About author:LI Pei-guan,born in 1996,B.S.His main research interests include machine learning and so on.
    HUANG Fang-wan,born in 1980,M.S.,senior lecturer,is a member of China Computer Federation.Her main research interests include computational intelligence,machine learning and big data analysis.
  • Supported by:
    The National Natural Science Foundation of China(61772136)and Research Project for Young and Middle-aged Teachers of Fujian Province (JT180045).

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Abstract: Data loss often occurs in the process of power load data collection,which adversely affects the accuracy of algorithm prediction.The existing missing data completion algorithm is only suitable for the case with less missing data,but performs poorly for the case with more missing data.Faced with the challenge of severe data loss,a method for power load missing data completion based on sparse representation is proposed.First of all,we assume that the data is randomly missing,and stitch the assumed missing data in the training data and the complete training data to form a training matrix.Secondly,an over-complete dictionary is generated by discrete cosine transform (DCT),and is learned according to the training matrix,aims to obtain a suitable dictionary for the best sparse representations of the samples in the training matrix.Finally,in the test phase,the upper part of the learned dictionary is used to obtain sparse representations of the missing data in the test set,and then the sparse representations and the lower part of the learned dictionary are used to reconstruct the complete data without missing.Experimental results show that using this method to complete missing values of power load data can achieve higher accuracy than traditional interpolation me-thods,correlation-based KNN algorithm,spatiotemporal compressed sensing estimation algorithm and time-series compressed sen-sing prediction algorithm.Even if the data miss rate is as high as 95%,this method can still effectively complete the missing data.

Key words: Data completion, Missing data, Power load, Sparse representation

CLC Number: 

  • TP181
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