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 halfhourly
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