  The recent advent of smart meters has led to large micro-level datasets. For
the first time, the electricity consumption at individual sites is available on
a near real-time basis. Efficient management of energy resources, electric
utilities, and transmission grids, can be greatly facilitated by harnessing the
potential of this data. The aim of this study is to generate probability
density estimates for consumption recorded by individual smart meters. Such
estimates can assist decision making by helping consumers identify and minimize
their excess electricity usage, especially during peak times. For suppliers,
these estimates can be used to devise innovative time-of-use pricing strategies
aimed at their target consumers. We consider methods based on conditional
kernel density (CKD) estimation with the incorporation of a decay parameter.
The methods capture the seasonality in consumption, and enable a nonparametric
estimation of its conditional density. Using eight months of half-hourly data
for one thousand meters, we evaluate point and density forecasts, for lead
times ranging from one half-hour up to a week ahead. We find that the
kernel-based methods outperform a simple benchmark method that does not account
for seasonality, and compare well with an exponential smoothing method that we
use as a sophisticated benchmark. To gauge the financial impact, we use density
estimates of consumption to derive prediction intervals of electricity cost for
different time-of-use tariffs. We show that a simple strategy of switching
between different tariffs, based on a comparison of cost densities, delivers
significant cost savings for the great majority of consumers.
