  Middle-term horizon (months to a year) power consumption prediction is a main
challenge in the energy sector, in particular when probabilistic forecasting is
considered. We propose a new modelling approach that incorporates trend,
seasonality and weather conditions, as explicative variables in a shallow
Neural Network with an autoregressive feature. We obtain excellent results for
density forecast on the one-year test set applying it to the daily power
consumption in New England U.S.A.. The quality of the achieved power
consumption probabilistic forecasting has been verified, on the one hand,
comparing the results to other standard models for density forecasting and, on
the other hand, considering measures that are frequently used in the energy
sector as pinball loss and CI backtesting.
