Neural network middle-term probabilistic forecasting of daily power consumption

Roberto Baviera and Michele Azzone

Abstract

Middle-term horizon (months to a year) power consumption prediction is a major challenge in the energy sector, particularly when probabilistic forecasting is considered. We propose a new modeling approach that incorporates trend, seasonality and weather conditions as explicative variables in a shallow neural network with an autoregressive feature. Applying it to the daily power consumption in New England, we obtain excellent results for the density forecast on the one-year test set. We verified the quality of the power consumption probabilistic forecasting achieved not only by comparing the results with other standard models for density forecasting but also by considering measures that are frequently used in the energy sector, such as the pinball loss function and confidence interval backtesting.