Open AccessRobust Graph Factorization for Multivariate Electricity Consumption Series Clustering
Author(s) -
Kaihong Zheng,
Liang Honghao,
Lukun Zeng,
Xiaowei Chen,
Sheng Li,
Hefang Jiang,
Qihang Gong,
Sijian Li,
Jingfeng Yang,
Shanshan Zhou
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/4310417
Subject(s) - cluster analysis , multivariate statistics , outlier , graph , embedding , data mining , computer science , electricity , series (stratigraphy) , factorization , econometrics , mathematics , artificial intelligence , algorithm , machine learning , theoretical computer science , engineering , paleontology , electrical engineering , biology
Multivariate electricity consumption series clustering can reflect trends of power consumption changes in the past time period, which can provide reliable guidance for electricity production. However, there are some abnormal series in the past multivariate electricity consumption series data, while outliers will affect the discovery of electricity consumption trends in different time periods. To address this problem, we propose a robust graph factorization model for multivariate electricity consumption clustering (RGF-MEC), which performs graph factorization and outlier discovery simultaneously. RGF-MEC first obtains a similarity graph by calculating distance among multivariate electricity consumption series data and then performs robust matrix factorization on the similarity graph. Meanwhile, the similarity graph is decomposed into a class-related embedding and a spectral embedding, where the class-related embedding directly reveals the final clustering results. Experimental results on realistic multivariate time-series datasets and multivariate electricity consumption series datasets demonstrate effectiveness of the proposed RGF-MEC model.