A comprehensive country-based day-ahead wind power generation forecast model by coupling numerical weather prediction data and CatBoost with feature selection methods for Turkey. Issue 5 (October 2022)

• Record Type: Journal Article
• Authors: Özen, Cem; Deniz, Ali
• Is Part Of: Wind engineering. Volume 46:Issue 5(2022)
• Page Start: 1359
• Page End: 1388
• Publication Date: 2022-10
• Subjects: Wind power forecast -- feature selection methods -- wind energy -- machine learning -- ERA5 -- ECMWF HRES; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X221078536
• Languages: English
• ISSNs: 0309-524X

Abstract

A country-based day-ahead wind power generation forecast (WPGF) model with a grid selection algorithm and feature selection models was proposed in this study. Atmospheric variables extracted from 300, 500, 700 hPa pressure levels, and surface level of ERA5 reanalysis data with 2.5° spatial resolution were used to train/validate the categorical boosting (CatBoost) model. A special grid selection algorithm was proposed by considering Turkey's spatial distribution of wind power plants. The day-ahead forecasts of ECMWF's HRES (High-resolution) were used as the test subset, therefore, paving the way for the operational use of the model. The proposed model could be considered much as a specialized machine learning based downscaling method for country-based WPGF due to using numerical weather prediction model outputs as its input. Results showed that the proposed model that uses fewer features has outperformed the other models with a normalized root mean square error of 7.6% and coefficient of determination of 0.8989.