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 language | English |
|---|---|
| Pages (from-to) | 1256-1272 |
| Number of pages | 17 |
| Journal | Wind Engineering |
| Volume | 45 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - Oct 2021 |
Bibliographical note
Publisher Copyright:© The Author(s) 2020.
Funding
We would like to express our special thanks to Sancak Energy for providing the data of the Yahyalı wind power plant. Furthermore, WRF model simulations and the training of the machine learning model; GBM were performed at the computers belong to the Department of the Meteorological Engineering of the İstanbul Technical University. The author(s) received no financial support for the research, authorship, and/or publication of this article.
Keywords
- WRF
- Wind power generation forecast
- gradient boosting machines
- hybrid model
- machine learning
- wind energy
