Research Area: Machine Learning
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.
Keywords:
Regression problems
Random Forest
Extreme Gradient Boosting
Ensemble methods
Support Vector
Author(s) Name: Alberto Torres-Barrán, Álvaro Alonso, Dorronsoro, José Ramόn
Journal name: Neurocomputing
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.neucom.2017.05.104
Volume Information: Volumes 326–327
