  Randomization-based Machine Learning methods for prediction are currently a
hot topic in Artificial Intelligence, due to their excellent performance in
many prediction problems, with a bounded computation time. The application of
randomization-based approaches to renewable energy prediction problems has been
massive in the last few years, including many different types of
randomization-based approaches, their hybridization with other techniques and
also the description of new versions of classical randomization-based
algorithms, including deep and ensemble approaches. In this paper we review the
most important characteristics of randomization-based machine learning
approaches and their application to renewable energy prediction problems. We
describe the most important methods and algorithms of this family of modeling
methods, and perform a critical literature review, examining prediction
problems related to solar, wind, marine/ocean and hydro-power renewable
sources. We support our critical analysis with an extensive experimental study,
comprising real-world problems related to solar, wind and hydro-power energy,
where randomization-based algorithms are found to achieve superior results at a
significantly lower computational cost than other modeling counterparts. We end
our survey with a prospect of the most important challenges and research
directions that remain open this field, along with an outlook motivating
further research efforts in this exciting research field.
