Journal
NEUROCOMPUTING
Volume 326, Issue -, Pages 151-160Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2017.05.104
Keywords
Ensembles; Regression; Random Forest; Gradient Boosting Regression; XGBoost; Wind energy; Solar radiation
Categories
Funding
- MINECO [TIN2013-42351-P, TIN2016-76406-P, TIN2015-70308-REDT]
- Comunidad de Madrid [S2013/ICE-2845 CASI-CAM-CM]
- FACIL (Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2016)
- UAMADIC Chair for Dat a Science and Machine Learning
- UAM-ADIC Chair for Data Science and Machine Learning
- FPU-MEC grant [AP-2012-5163]
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The ability of ensemble models to retain the bias of their learners while decreasing their individual variance has long made them quite attractive in a number of classification and regression problems. In this work we will study the application of Random Forest Regression (RFR), Gradient Boosted Regression (GBR) and Extreme Gradient Boosting (XGB) to global and local wind energy prediction as well as to a solar radiation problem. Besides a complete exploration of the fundamentals of RFR, GBR and XGB, we will show experimentally that ensemble methods can improve on Support Vector Regression (SVR) for individual wind farm energy prediction, that GBR and XGB are competitive when the interest lies in predicting wind energy in a much larger geographical scale and, finally, that both gradient-based ensemble methods can improve on SVR in the solar radiation problem. (C) 2017 Elsevier B.V. All rights reserved.
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