Deep joint optimized clustering model for life cycle identification of enterprise electricity consumption

Abstract

Motivation analysis is significant in inspecting illegal power consumption behavior. Based on enterprise life cycle theory, this study proposes enterprise electricity consumption life cycle to conceptually aid the analysis of motives for enterprise illegal power consumption behavior. The identification of the aforementioned life cycle is achieved by clustering. We propose a deep clustering model based on the joint optimization of Gated Recurrent Unit (GRU) autoencoder and an improved K-prototypes algorithm. The proposed model utilizes GRU autoencoder to learn the latent features of high-dimensional enterprise electricity data and dynamically adjust the distance metric in K-prototypes algorithm based on the reconstruction error of GRU autoencoder. By jointly optimizing the two components, the proposed model can effectively cluster the enterprise electricity consumers and enterprises’ electricity consumption life cycle is identified based on the features and patterns of enterprises in each cluster. Experiment demonstrates that the proposed model can effectively capture the non-linear transformation from electricity data to its latent space and yield superior clustering results, and can provide data support for the identification of enterprise electricity consumption life cycle.