  This Ph.D. thesis deals with the optimization of several renewable energy
resources development as well as the improvement of facilities management in
oceanic engineering and airports, using computational hybrid methods belonging
to AI to this end. Energy is essential to our society in order to ensure a good
quality of life. This means that predictions over the characteristics on which
renewable energies depend are necessary, in order to know the amount of energy
that will be obtained at any time. The second topic tackled in this thesis is
related to the basic parameters that influence in different marine activities
and airports, whose knowledge is necessary to develop a proper facilities
management in these environments. Within this work, a study of the
state-of-the-art Machine Learning have been performed to solve the problems
associated with the topics above-mentioned, and several contributions have been
proposed: One of the pillars of this work is focused on the estimation of the
most important parameters in the exploitation of renewable resources. The
second contribution of this thesis is related to feature selection problems.
The proposed methodologies are applied to multiple problems: the prediction of
$H_s$, relevant for marine energy applications and marine activities, the
estimation of WPREs, undesirable variations in the electric power produced by a
wind farm, the prediction of global solar radiation in areas from Spain and
Australia, really important in terms of solar energy, and the prediction of
low-visibility events at airports. All of these practical issues are developed
with the consequent previous data analysis, normally, in terms of
meteorological variables.
