  The high penetration of volatile renewable energy sources such as solar make
methods for coping with the uncertainty associated with them of paramount
importance. Probabilistic forecasts are an example of these methods, as they
assist energy planners in their decision-making process by providing them with
information about the uncertainty of future power generation. Currently, there
is a trend towards the use of deep learning probabilistic forecasting methods.
However, the point at which the more complex deep learning methods should be
preferred over more simple approaches is not yet clear. Therefore, the current
article presents a simple comparison between a long short-term memory neural
network and other more simple approaches. The comparison consists of training
and comparing models able to provide one-day-ahead probabilistic forecasts for
a solar power system. Moreover, the current paper makes use of an open-source
dataset provided during the Global Energy Forecasting Competition of 2014
(GEFCom14).
