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.
| Original language | English |
|---|---|
| Pages (from-to) | 1359-1388 |
| Number of pages | 30 |
| Journal | Wind Engineering |
| Volume | 46 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - Oct 2022 |
Bibliographical note
Publisher Copyright:© The Author(s) 2022.
Funding
We would like to thank Dr. Deniz Demirhan and ECMWF for providing ECMWF-HRES data which enable us to evaluate the operational process of the proposed model. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study has resulted as a part of a project named “Building a Short-term Wind Power Generation Forecast Model by Coupling Numerical Weather Prediction Models and Machine Learning Algorithms” which is supported within the scope of PhD thesis projects of the Scientific Research Projects (BAP) Institution of the Istanbul Technical University with a grant number of MDK-2020-42646. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study has resulted as a part of a project named “Building a Short-term Wind Power Generation Forecast Model by Coupling Numerical Weather Prediction Models and Machine Learning Algorithms” which is supported within the scope of PhD thesis projects of the Scientific Research Projects (BAP) Institution of the Istanbul Technical University with a grant number of MDK-2020-42646.
| Funders | Funder number |
|---|---|
| Istanbul Teknik Üniversitesi | MDK-2020-42646 |
| European Centre for Medium-Range Weather Forecasts |
Keywords
- ECMWF HRES
- ERA5
- Wind power forecast
- feature selection methods
- machine learning
- wind energy
