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