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Energies 2016, 9(8), 607; doi:10.3390/en9080607

A Review of Classification Problems and Algorithms in Renewable Energy Applications

1
Department of Quantitative Methods, Universidad Loyola Andalucía, 14004 Córdoba, Spain
2
Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
3
Department of Computer Science and Numerical Analysis, Universidad de Córdoba, 14071 Córdoba, Spain
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Chunhua Liu
Received: 17 May 2016 / Revised: 21 July 2016 / Accepted: 22 July 2016 / Published: 2 August 2016
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Abstract

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field. View Full-Text
Keywords: classification algorithms; machine learning; renewable energy; applications classification algorithms; machine learning; renewable energy; applications
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pérez-Ortiz, M.; Jiménez-Fernández, S.; Gutiérrez, P.A.; Alexandre, E.; Hervás-Martínez, C.; Salcedo-Sanz, S. A Review of Classification Problems and Algorithms in Renewable Energy Applications. Energies 2016, 9, 607.

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