  Future grid management systems will coordinate distributed production and
storage resources to manage, in a cost effective fashion, the increased load
and variability brought by the electrification of transportation and by a
higher share of weather dependent production. Electricity demand forecasts at a
low level of aggregation will be key inputs for such systems. We focus on
forecasting demand at the individual household level, which is more challenging
than forecasting aggregate demand, due to the lower signal-to-noise ratio and
to the heterogeneity of consumption patterns across households. We propose a
new ensemble method for probabilistic forecasting, which borrows strength
across the households while accommodating their individual idiosyncrasies. In
particular, we develop a set of models or 'experts' which capture different
demand dynamics and we fit each of them to the data from each household. Then
we construct an aggregation of experts where the ensemble weights are estimated
on the whole data set, the main innovation being that we let the weights vary
with the covariates by adopting an additive model structure. In particular, the
proposed aggregation method is an extension of regression stacking (Breiman,
1996) where the mixture weights are modelled using linear combinations of
parametric, smooth or random effects. The methods for building and fitting
additive stacking models are implemented by the gamFactory R package, available
at https://github.com/mfasiolo/gamFactory.
