  The increasing penetration of embedded renewables makes forecasting net-load,
consumption less embedded generation, a significant and growing challenge. Here
a framework for producing probabilistic forecasts of net-load is proposed with
particular attention given to the tails of predictive distributions, which are
required for managing risk associated with low-probability events. Only small
volumes of data are available in the tails, by definition, so estimation of
predictive models and forecast evaluation requires special attention. We
propose a solution based on a best-in-class load forecasting methodology
adapted for net-load, and model the tails of predictive distributions with the
Generalised Pareto Distribution, allowing its parameters to vary smoothly as
functions of covariates. The resulting forecasts are shown to be calibrated and
sharper than those produced with unconditional tail distributions. In a
use-case inspired evaluation exercise based on reserve setting, the conditional
tails are shown to reduce the overall volume of reserve required to manage a
given risk. Furthermore, they identify periods of high risk not captured by
other methods. The proposed method therefore enables user to both reduce costs
and avoid excess risk.
