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Boosting algorithms in energy research: a systematic review

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Abstract

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.

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Acknowledgements

We thank the Associate Editor and the Reviewers for their constructive comments and suggestions, which helped us to substantially improve the manuscript.

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  1. Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80, Zografou, Greece

    Hristos Tyralis & Georgia Papacharalampous

  2. Air Force Support Command, Hellenic Air Force, Elefsina Air Base, 192 00, Elefsina, Greece

    Hristos Tyralis

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  1. Hristos Tyralis
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  2. Georgia Papacharalampous
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Tyralis, H., Papacharalampous, G. Boosting algorithms in energy research: a systematic review. Neural Comput & Applic 33, 14101–14117 (2021). https://doi.org/10.1007/s00521-021-05995-8

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