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.
Keywords
Load Forecasting
Electricity
SENSIBLE project
reliable probabilistic forecasts
probabilistic day-ahead forecasting model
electricity demand forecasting
home energy management systems
household electricity demand
smart meter data
smart grid demonstration site
hourly electrical demand prediction
temperature predictions
smart-grids context
smart power grids
probability
European project
probabilistic forecasting
Origin : Files produced by the author(s)
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