Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression

Souhaib Ben Taieb, Raphaël Huser, Rob J. Hyndman, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

148 Scopus citations


Smart electricity meters are currently deployed in millions of households to collect detailed individual electricity consumption data. Compared with traditional electricity data based on aggregated consumption, smart meter data are much more volatile and less predictable. There is a need within the energy industry for probabilistic forecasts of household electricity consumption to quantify the uncertainty of future electricity demand in order to undertake appropriate planning of generation and distribution. We propose to estimate an additive quantile regression model for a set of quantiles of the future distribution using a boosting procedure. By doing so, we can benefit from flexible and interpretable models, which include an automatic variable selection. We compare our approach with three benchmark methods on both aggregated and disaggregated scales using a smart meter data set collected from 3639 households in Ireland at 30-min intervals over a period of 1.5 years. The empirical results demonstrate that our approach based on quantile regression provides better forecast accuracy for disaggregated demand, while the traditional approach based on a normality assumption (possibly after an appropriate Box-Cox transformation) is a better approximation for aggregated demand. These results are particularly useful since more energy data will become available at the disaggregated level in the future.
Original languageEnglish (US)
Pages (from-to)2448-2455
Number of pages8
JournalIEEE Transactions on Smart Grid
Issue number5
StatePublished - Mar 2 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


Dive into the research topics of 'Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression'. Together they form a unique fingerprint.

Cite this