Wind power prediction using bootstrap aggregating trees approach to enabling sustainable wind power integration in a smart grid
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2019-CRG7-3800
Online Publication Date2019-10-09
Print Publication Date2019-12
Embargo End Date2021-10-09
Permanent link to this recordhttp://hdl.handle.net/10754/659075
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AbstractPrecise prediction of wind power is important in sustainably integrating the wind power in a smart grid. The need for short-term predictions is increased with the increasing installed capacity. The main contribution of this work is adopting bagging ensembles of decision trees approach for wind power prediction. The choice of this regression approach is motivated by its ability to take advantage of many relatively weak single trees to reach a high prediction performance compared to single regressors. Moreover, it reduces the overall error and has the capacity to merge numerous models. The performance of bagged trees for predicting wind power has been compared to four commonly know prediction methods namely multivariate linear regression, support vector regression, principal component regression, and partial least squares regression. Real measurements recorded every ten minutes from an actual wind turbine are used to illustrate the prediction quality of the studied methods. Results showed that the bagged trees regression approach reached the highest prediction performance with a coefficient of determination of 0.982. The result showed that the bagged trees approach is followed by support vector regression with Gaussian kernel, the same model when using a quadratic kernel, and the multivariate linear regression, partial least squares, and principal component regression gave the lowest prediction. The investigated models in this study can represent a helpful tool for model-based anomaly detection in wind turbines.
CitationHarrou, F., Saidi, A., & Sun, Y. (2019). Wind power prediction using bootstrap aggregating trees approach to enabling sustainable wind power integration in a smart grid. Energy Conversion and Management, 201, 112077. doi:10.1016/j.enconman.2019.112077
SponsorsThis publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.
JournalEnergy Conversion and Management