Probabilistic Day-Ahead Forecasting of Household Electricity Demand - Archive ouverte HAL Access content directly
Conference Papers Year : 2017

Probabilistic Day-Ahead Forecasting of Household Electricity Demand

Abstract

Forecasting electricity demand at the local level of a building up to a feeder is increasingly necessary in several applications in the smart-grids context. Actors like aggregators and retailers, and tools like home energy management systems, require such forecasts as input. In this paper, a probabilistic day-ahead forecasting model is proposed to predict hourly electrical demand from individual households. This stochastic model uses smart-meter data and temperature predictions to make quantile forecasts. Performance is evaluated using data from a real-life smart grid demonstration site developed in Évora, Portugal as part of the European project SENSIBLE. The proposed model consistently outperforms a persistence model and provides reliable probabilistic forecasts.
Fichier principal
Vignette du fichier
CIRED_2017_0625.pdf (423.15 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01518373 , version 1 (04-05-2017)

Identifiers

Cite

Alexis Gerossier, Robin Girard, Georges Kariniotakis, Andrea Michiorri. Probabilistic Day-Ahead Forecasting of Household Electricity Demand. CIRED 2017 - 24th International Conference on Electricity Distribution, Jun 2017, Glasgow, United Kingdom. pp.2500 - 2504, ⟨10.1049/oap-cired.2017.0625⟩. ⟨hal-01518373⟩
447 View
452 Download

Altmetric

Share

Gmail Facebook X LinkedIn More