Issue |
E3S Web Conf.
Volume 520, 2024
4th International Conference on Environment Resources and Energy Engineering (ICEREE 2024)
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Article Number | 03005 | |
Number of page(s) | 8 | |
Section | New Progress in New Energy and Resource Utilization Technology | |
DOI | https://doi.org/10.1051/e3sconf/202452003005 | |
Published online | 03 May 2024 |
Research on Numerical Weather Prediction Wind Speed Correction Based on Stacking Ensemble Learning Algorithm
1 School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
2 Zhengzhou Institute of Metrology, Zhengzhou 450001, China
3 National Institute of Metrology, Beijing 100029, China
* Corresponding author’s email: reng@nim.ac.cn
The transportation of wind fields plays a crucial role in the dispersion and distribution of air pollutants, and accurate wind speed prediction is essential for assessing pollutant concentrations. In this study, we constructed a Stacking ensemble learning model using three models, namely Random Forest, LightGBM, and XGBoost, as base learners, and the LASSO regression model as the meta-learner to optimize wind speed data forecasting for Zhengzhou City’s urban area using WRF. Firstly, based on Pearson correlation coefficients, we selected meteorological variables that have a significant impact on near-surface wind speed and derived historical lagged features. Bayesian TPE was utilized for hyperparameter tuning and model building. Finally, the performance of the trained model was evaluated by comparing it with ground observation data. The results showed that compared to the WRF model, Random Forest, LightGBM, and XGBoost effectively reduced forecast errors and significantly improved wind speed predictions. Both LightGBM and XGBoost demonstrated similar performance in the correction models across the 11 stations and outperformed Random Forest. The Stacking method integrated the advantages of the base learners and exhibited improvement capabilities over individual models, highlighting the potential of machine learning in enhancing localized and accurate weather forecasting.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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