  We study the forecasting of the power consumptions of a population of
households and of subpopulations thereof. These subpopulations are built
according to location, to exogenous information and/or to profiles we
determined from historical households consumption time series. Thus, we aim to
forecast the electricity consumption time series at several levels of
households aggregation. These time series are linked through some summation
constraints which induce a hierarchy. Our approach consists in three steps:
feature generation, aggregation and projection. Firstly (feature generation
step), we build, for each considering group for households, a benchmark
forecast (called features), using random forests or generalized additive
models. Secondly (aggregation step), aggregation algorithms, run in parallel,
aggregate these forecasts and provide new predictions. Finally (projection
step), we use the summation constraints induced by the time series underlying
hierarchy to re-conciliate the forecasts by projecting them in a well-chosen
linear subspace. We provide some theoretical guaranties on the average
prediction error of this methodology, through the minimization of a quantity
called regret. We also test our approach on households power consumption data
collected in Great Britain by multiple energy providers in the Energy Demand
Research Project context. We build and compare various population segmentations
for the evaluation of our approach performance.
