%0 Journal Article 
%A Aybar Ruiz, Adrián
%T Effective Neuro-Evolutionary Schemes for Solar Radiation Estimation Problems
%D 2021
%U http://hdl.handle.net/10017/51087
%X This Ph.D. thesis’ goal is focused on the optimization of renewable energy resources development,
specifically solar PV energy, using different hybrid computational Machine Learning
techniques.
Energy is the engine of our society, allowing us performing almost every action taken by
human beings in our daily routine, and providing a constant evolution and development in all
our fields. Currently, fossil fuels entail the higher percentage of energy sources in our planet.
They have several advantages, such as easy and constant production, but, at the same time, they
present substantial disadvantages, like the extreme pollution associated with these resources,
and their contribution to global warming and climate change. This is the reason why the
largest and most powerful economies are working for a energy change towards renewable sources
for a sustainable development. In the introduction of this thesis, a large number of studies
are presented, which foresee a penetration by over a 50% of this kind of energies in the next
decades. Strong investments are being made in this field, looking for technology development
and, besides, introducing these energies into society as a matter to be taken into account, for it is
related to economic and social status. However, the development of energy systems mainly based
on renewable energy will surely be slow, since these energies depend on variables which are out
of our control, mainly atmospheric and climatic variables, which are intrinsically intermittent.
This matter must be taken into account, due to the amount of energy demanded by society at
the present, as well as the tremendous increase that is predicted for this demand in the future,
due to new technologies and new ways of daily routines, like electric vehicles or IoT, etc.
To obtain a solution for this problem, on one hand, it is fundamental to achieve the capability
of predicting the quantity of energy obtained in each moment, avoiding increases or decreases of
energy, being this matter the core of this Ph.D. Thesis, where the optimal feature selection for
predicting the quantity of global solar radiation in a given point is studied. On the other hand,
all the information to do the prediction process will be obtained from a numerical weather mesoscale
model called WRF (Weather Research and Forecasting), a static model based on different
physic equations which involve different variables like humidity, pressure or percentage of cloud
fraction in any point and different heights in the planet. Additionally, dynamic information,
like global solar radiation can be obtained from a radiometric measuring point in Toledo, Spain,
allowing us to get a database of the solar global radiation in the past few years. The result of
9
mixing both of these data will be added as inputs in our hybrid systems.
In this work, a deep analysis in the state of art for machine learning models is performed, so
as to solve the problems previously considered. Different contributions have been proposed:
1. One of the pillars of this work is focused on the optimal feature selection in the exploitation
of solar PV radiation in a given point. For this purpose, Extreme-Learning Machine (ELM)
will be used as regressor element in the system, where the output of the ELM will be
calculated from the WRF outcome features added as inputs in the system.
2. The second contribution of this thesis is related to parameters selection problems. More
specifically, the use of EAs such as Grouping Genetic Algorithm (GGA) or Coral Reef
Optimization (CRO) hybridized with others ML are used as classifiers and regressors.
Regarding this, the GGA or CRO look for several subsets of basic parameters to solve the
problem, and the regressor employed provides the prediction in terms of the selected by the
Genetic Algorithm (GA), reducing the computational cost maintaining a good accuracy.
Finally, the several of the mentioned algorithms are applied in the same problem already
defined, in order to get the global solar radiation prediction in different points, dealing to improve
previous results in other works and obtaining new applications and techniques, as new paths of
research in the future.

%K Inteligencia Artificial
%K Heurística
%K Recursos Renovables
%K Energía solar
%K Telecomunicaciones
%K Telecommunication
%~ Biblioteca Universidad de Alcala