Wind power generation forecast by coupling numerical weather prediction model and gradient boosting machines in Yahyalı wind power plant

Cem Özen*, Umur Dinç, Ali Deniz, Haldun Karan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Forecasting of the wind speed and power generation for a wind farm has always been quite challenging and has importance in terms of balancing the electricity grid and preventing energy imbalance penalties. This study focuses on creating a hybrid model that uses both numerical weather prediction model and gradient boosting machines (GBM) for wind power generation forecast. Weather Research and Forecasting (WRF) model with a low spatial resolution is used to increase temporal resolutions of the computed new or existing variables whereas GBM is used for downscaling purposes. The results of the hybrid model have been compared with the outputs of a stand-alone WRF which is well configured in terms of physical schemes and has a high spatial resolution for Yahyalı wind farm over a complex terrain located in Turkey. Consequently, the superiority of the hybrid model in terms of both performance indicators and computational expense in detail is shown.

Original languageEnglish
Pages (from-to)1256-1272
Number of pages17
JournalWind Engineering
Volume45
Issue number5
DOIs
Publication statusPublished - Oct 2021

Bibliographical note

Publisher Copyright:
© The Author(s) 2020.

Keywords

  • gradient boosting machines
  • hybrid model
  • machine learning
  • wind energy
  • Wind power generation forecast
  • WRF

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