  Buildings consume over 40% of the total energy in modern societies, and
improving their energy efficiency can significantly reduce our energy
footprint. In this paper, we present \texttt{WattScale}, a data-driven approach
to identify the least energy-efficient buildings from a large population of
buildings in a city or a region. Unlike previous methods such as least-squares
that use point estimates, \texttt{WattScale} uses Bayesian inference to capture
the stochasticity in the daily energy usage by estimating the distribution of
parameters that affect a building. Further, it compares them with similar homes
in a given population. \texttt{WattScale} also incorporates a fault detection
algorithm to identify the underlying causes of energy inefficiency. We validate
our approach using ground truth data from different geographical locations,
which showcases its applicability in various settings. \texttt{WattScale} has
two execution modes -- (i) individual, and (ii) region-based, which we
highlight using two case studies. For the individual execution mode, we present
results from a city containing >10,000 buildings and show that more than half
of the buildings are inefficient in one way or another indicating a significant
potential from energy improvement measures. Additionally, we provide probable
cause of inefficiency and find that 41\%, 23.73\%, and 0.51\% homes have poor
building envelope, heating, and cooling system faults, respectively. For the
region-based execution mode, we show that \texttt{WattScale} can be extended to
millions of homes in the US due to the recent availability of representative
energy datasets.
