  The aim of this paper is to study the asymptotic properties of a class of
kernel conditional mode estimates whenever functional stationary ergodic data
are considered. To be more precise on the matter, in the ergodic data setting,
we consider a random element $(X, Z)$ taking values in some semi-metric
abstract space $E\times F$. For a real function $\varphi$ defined on the space
$F$ and $x\in E$, we consider the conditional mode of the real random variable
$\varphi(Z)$ given the event $``X=x"$. While estimating the conditional mode
function, say $\theta_\varphi(x)$, using the well-known kernel estimator, we
establish the strong consistency with rate of this estimate uniformly over
Vapnik-Chervonenkis classes of functions $\varphi$. Notice that the ergodic
setting offers a more general framework than the usual mixing structure. Two
applications to energy data are provided to illustrate some examples of the
proposed approach in time series forecasting framework. The first one consists
in forecasting the {\it daily peak} of electricity demand in France (measured
in Giga-Watt). Whereas the second one deals with the short-term forecasting of
the electrical {\it energy} (measured in Giga-Watt per Hour) that may be
consumed over some time intervals that cover the peak demand.
