Open AccessA dimensionality reduction method of power load data based on the combination of VMD-OMP-Kmeans
Author(s) -
Tong Li,
Jinliang Song,
Jian Chen,
Haosong Tong,
Jia Cui,
Anni Wang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/615/1/012057
Subject(s) - dimensionality reduction , matching pursuit , computer science , data set , dimension (graph theory) , curse of dimensionality , cluster analysis , power (physics) , reduction (mathematics) , algorithm , set (abstract data type) , filter (signal processing) , k means clustering , pattern recognition (psychology) , data mining , artificial intelligence , mathematics , physics , geometry , quantum mechanics , compressed sensing , pure mathematics , computer vision , programming language
With the power data becoming more and more complex, it is of great significance to reduce the dimension and reconstruct the power data for the subsequent processing and application of power big data. This paper presents an algorithm based on variational mode decomposition (VMD) decomposition and orthogonal matching pursuit (OMP) reconstruction to reduce the dimension of power load data. Firstly, VMD method is used to decompose and filter the power load data; Secondly, K-means clustering algorithm is used to synthesize the decomposed data mode into a cluster center data set. Then, the data set is used as a dictionary; Thirdly, an OMP algorithm is proposed to select the elements to be reconstructed in the dictionary. And the corresponding data weights are given to reconstruct the load data with high-precision. As a result, the dimensionality of massive load data is reduced automatically. Finally, this paper builds a flexible load data dimensionality reduction model based on VMD method to verify the proposed method.