Probabilistic Day-Ahead Forecasting of Household Electricity DemandReportar como inadecuado

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1 PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques

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.

Keywords : Forecast Electricity

Autor: Alexis Gerossier - Robin Girard - Georges Kariniotakis - Andrea Michiorri -



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