This study implements a hybrid unsupervised machine learning approach for detecting anomalies in electricity consumption patterns from Advanced Metering Infrastructure (AMI) systems. The proposed methodology integrates dimensionality reduction techniques using Principal Component Analysis (PCA) with the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm to identify anomalous behavior. The dataset, which encompasses 26,230 m and spans a one-year period, is segmented based on user type for the purpose of analysis. The findings indicate a high degree of temporal stability in residential consumption patterns, with detection performance varying depending on the nature of the simulated anomaly. The “Sudden Drop” anomaly pattern shows an average detection rate of 61%, with monthly peaks reaching up to 96%, while more subtle anomalies such as flattening remain considerably harder to identify, with detection rates ranging between 4% and 35%. These findings contribute to the development of automated surveillance systems for reducing non-technical losses in electrical distribution networks.
González et al. (Wed,) studied this question.