Robust Graph Factorization for Multivariate Electricity Consumption Series Clustering

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.