Wind Power Generation Forecast by Coupling Numerical Weather Prediction Model and Gradient Boosting Machines

A low-resolution WRF model has run which has exact same spatial resolution with GFS of NCEP which is initial and boundary conditions data and extracted data from the four grid points of this model which surround the related wind farm are used as predictor variables to the gradient boosting machines. Thus; the downscaling process is basically done by the coupling of the low-resolution numerical weather prediction model and machine learning algorithm. The results of this hybrid model are also compared with the results of a WRF model which has a higher spatial resolution in terms of both statistical measures and required time. As a result of this; the study claims a novel approach for not only increasing the accuracy of the wind power generation forecasts but also reducing the computational expense. Since wind energy increases its share in installed energy capacity thanks to its maturity in terms of technology and decreasing costs; wind power generation forecasts with high performance becomes very crucial to have not only the security of the electricity supply of the countries all around the world but also prevent energy imbalance penalties for the wind farm owners. Besides, this study shows that a better performance than the downscaling processes of the numerical weather prediction models do can be achieved in shorter periods with machine learning algorithms. Thus; this study also highlights that the coupling of machine learning algorithms with the numerical weather prediction models can play an effective role in the future in terms of atmospheric sciences. Consequently; this work provides basically not only increasing the accuracy of the wind power generation forecasts when it is compared with the numerical weather prediction models but also decreasing the computational expense by doing the downscaling process of numerical weather prediction models with gradient boosting machines.