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This paper presents a novel data-driven method that determines the dailyconsumption patterns of customers without smart meters (SMs) to enhance theobservability of distribution systems. Using the proposed method, the dailyconsumption of unobserved customers is extracted from their monthly billingdata based on three machine learning models: first, a spectral clustering (SC)algorithm is used to infer the typical daily load profiles of customers withSMs. Each typical daily load behavior represents a distinct class of customerbehavior. In the second module, a multi-timescale learning (MTSL) model istrained to estimate the hourly consumption using monthly energy data for thecustomers of each class. The third stage leverages a recursive Bayesianlearning (RBL) method and branch current state estimation (BCSE) residuals toestimate the daily load profiles of unobserved customers without SMs. Theproposed data-driven method has been tested and verified using real utilitydata.

A Multi-Timescale Data-Driven Approach to Enhance Distribution System Observability