EleMoE: An Unsupervised User Profiling Framework for Electricity Consumption Behavior

Abstract

The proliferation of smart grids has led to an abundance of unlabeled residential electricity consumption time series data, which contains rich user behavior information. However, traditional profiling methods struggle to capture its inherent complexity. This paper proposes a novel deep self-supervised learning framework, hereafter referred to as Electricity Mixture of Experts (EleMoE), based on a Mixture of Experts (MoE) architecture for unsupervised profiling of residential electricity consumption behavior. Our framework innovatively integrates various complementary time series deep learning models (including MLP, LSTM, TCN, Transformer, and Mamba) as “experts.” These experts are trained via a self-supervised task of time series reconstruction to learn deep embeddings of consumption patterns. The MoE architecture enables the model to dynamically select or combine experts based on the characteristics of input segments, thereby capturing the complexity and diversity of electricity consumption behavior more comprehensively and robustly. After obtaining high-quality embeddings, we employ the K-Means algorithm for unsupervised clustering. The optimal number of clusters is systematically determined using analytical techniques like the Elbow and Silhouette methods, enhancing the objectivity of the profiling. Experimental results demonstrate that our proposed framework exhibits superior performance. Through in-depth analysis of average load curves and multi-dimensional radar charts, we successfully identify and explain various business-valuable residential electricity consumption profiles. This research offers an efficient, automated, interpretable, and robust new methodology for user behavior analysis in smart grids.