Additive stacking for disaggregate electricity demand forecasting

TL;DR

This paper introduces an innovative additive stacking ensemble method for probabilistic household-level electricity demand forecasting, effectively capturing individual consumption patterns while leveraging cross-household information.

Contribution

It develops a novel additive stacking approach with covariate-dependent weights for improved disaggregate demand forecasts, implemented in the gamFactory R package.

Findings

Enhanced forecast accuracy at the household level.

Effective modeling of heterogeneous consumption patterns.

Flexible ensemble weights adapt to covariates.

Abstract

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.