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

Ozen C., Dinc U., Deniz A., Karan H.

WIND ENGINEERING, vol.45, no.5, pp.1256-1272, 2021 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 45 Issue: 5
  • Publication Date: 2021
  • Doi Number: 10.1177/0309524x20972115
  • Journal Name: WIND ENGINEERING
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Environment Index, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.1256-1272
  • Keywords: gradient boosting machines, hybrid model, machine learning, wind energy, Wind power generation forecast, WRF
  • Istanbul Technical University Affiliated: Yes

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 Yahyali 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.