  Machine learning algorithms have been extensively exploited in energy
research, due to their flexibility, automation and ability to handle big data.
Among the most prominent machine learning algorithms are the boosting ones,
which are known to be "garnering wisdom from a council of fools", thereby
transforming weak learners to strong learners. Boosting algorithms are
characterized by both high flexibility and high interpretability. The latter
property is the result of recent developments by the statistical community. In
this work, we provide understanding on the properties of boosting algorithms to
facilitate a better exploitation of their strengths in energy research. In this
respect, (a) we summarize recent advances on boosting algorithms, (b) we review
relevant applications in energy research with those focusing on renewable
energy (in particular those focusing on wind energy and solar energy)
consisting a significant portion of the total ones, and (c) we describe how
boosting algorithms are implemented and how their use is related to their
properties. We show that boosting has been underexploited so far, while great
advances in the energy field are possible both in terms of explanation and
interpretation, and in terms of predictive performance.
