  Probabilistic forecasting of power consumption in a middle-term horizon
(months to a year) is a main challenge in the energy sector. It plays a key
role in planning future generation plants and transmission grid. We propose a
new model that incorporates trend and seasonality features as in traditional
time-series analysis and weather conditions as explicative variables in a
parsimonious machine learning approach, known as Gaussian Process. Applying to
a daily power consumption dataset in North East England provided by one of the
largest energy suppliers, we obtain promising results in Out-of-Sample density
forecasts up to one year, even using a small dataset, with only a two-year
In-Sample data. In order to verify the quality of the achieved power
consumption probabilistic forecast we consider measures that are common in the
energy sector as pinball loss and Winkler score and backtesting conditional and
unconditional tests, standard in the banking sector after the introduction of
Basel II Accords.
