ADDITIVE STACKING FOR DISAGGREGATE ELECTRICITY DEMAND FORECASTING

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 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.

Automated summary

Future grid management systems will manage increasing loads and variability caused by the electrification of transportation and weather-dependent production by coordinating distributed production and storage resources. The researchers propose a new ensemble method for probabilistic forecasting at the individual household level, which adapts to the diverse demand dynamics across households by utilizing a set of models capturing different patterns and adapting weights based on covariates.