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Article

Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm

by 1, 2,3 and 1,*
1
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
2
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Yucheng Comprehensive Experiment Station, Chinese Academy of Science, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(7), 738; https://doi.org/10.3390/atmos11070738
Received: 18 March 2020 / Revised: 1 July 2020 / Accepted: 9 July 2020 / Published: 12 July 2020
With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results. View Full-Text
Keywords: wind speed forecast; numerical weather prediction; post processing; gradient boosting decision tree wind speed forecast; numerical weather prediction; post processing; gradient boosting decision tree
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MDPI and ACS Style

Xu, W.; Ning, L.; Luo, Y. Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm. Atmosphere 2020, 11, 738. https://doi.org/10.3390/atmos11070738

AMA Style

Xu W, Ning L, Luo Y. Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm. Atmosphere. 2020; 11(7):738. https://doi.org/10.3390/atmos11070738

Chicago/Turabian Style

Xu, Wenqing, Like Ning, and Yong Luo. 2020. "Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm" Atmosphere 11, no. 7: 738. https://doi.org/10.3390/atmos11070738

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