Using Smart Meters to Learn Water Customer Behavior

This article addresses the need to divide a population of water utility customers into groups based on their similarities and differences, using only the measured flow data collected by water meters. After clustering, the groups represent customers with similar consumption behavior patterns and provide insight into “normal” and “unusual” customer behavior patterns for individually metered water utility customers within North America. The contributions of this work not only represent a novel work, but also solve a practical problem for the utility industry. This article introduces a method of agglomerative clustering using information theoretic distance measures on Gaussian mixture models within a reconstructed phase space, designed to accommodate a utility's limited human, financial, computational, and environmental resources. The proposed weighted variation of information distance measure for comparing Gaussian mixture models emphasizes those behaviors whose statistical distributions are more compact over those behaviors with large variation and contributes a novel addition to existing comparison options. We conduct several experiments with both synthetic and real data to show the reasonableness of the clustering results and their consistency.