  The aim of this paper is to compute one-day-ahead prediction regions for
daily curves of electricity demand and price. Three model-based procedures to
construct general prediction regions are proposed, all of them using bootstrap
algorithms. The first proposed method considers any $L_p$ norm for functional
data to measure the distance between curves, the second one is designed to take
different variabilities along the curve into account, and the third one takes
advantage of the notion of depth of a functional data. The regression model
with functional response on which our proposed prediction regions are based is
rather general: it allows to include both endogenous and exogenous functional
variables, as well as exogenous scalar variables; in addition, the effect of
such variables on the response one is modeled in a parametric, nonparametric or
semi-parametric way. A comparative study is carried out to analyse the
performance of these prediction regions for the electricity market of mainland
Spain, in year 2012. This work extends and complements the methods and results
in Aneiros et al. (2016) (focused on curve prediction) and Vilar et al. (2018)
(focused on prediction intervals), which use the same database as here.
