SHORT-TERM 100-METERS WIND SPEED FORECAST CORRECTION METHOD BASED ON MACHINE LEARNING

Gao Jinbing; Cao Rundong; Yu Tingzhao; Yao Jinfeng; Shen Yanbo

doi:10.19912/j.0254-0096.tynxb.2023-1243

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 71-77. DOI: 10.19912/j.0254-0096.tynxb.2023-1243

SHORT-TERM 100-METERS WIND SPEED FORECAST CORRECTION METHOD BASED ON MACHINE LEARNING

Gao Jinbing^1-3, Cao Rundong^1-3, Yu Tingzhao^1,3, Yao Jinfeng^1,2, Shen Yanbo^1-3

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Abstract

In this paper, a forecast correction method based on machine learning is proposed, which first corrects regional errors with gridded analysis products and then corrects local errors with station observation data. The method utilizes the gridded wind speed analysis products at 100-meter height to correct the results of numerical weather prediction through deep learning algorithms, and then corrects the station wind speed forecast results with a random forest integrated forecast approach based on the observation data of wind speed at 100-meter height from the anemometer tower. An empirical analysis of a meteorological station in central Inner Mongolia shows that after correction with the proposed method, the root mean square error （RMSE） of wind speed prediction for the next 84 hours is significantly reduced from 4.25 m/s to 3.16-3.79 m/s after gridded forecast correction and to 2.81 m/s after station integrated correction. Therefore, the proposed method can effectively reduce forecast errors.

Key words

wind speed forecast at 100-meter height / forecast correction / machine learning / deep learning / integrated forecast

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Gao Jinbing, Cao Rundong, Yu Tingzhao, Yao Jinfeng, Shen Yanbo. SHORT-TERM 100-METERS WIND SPEED FORECAST CORRECTION METHOD BASED ON MACHINE LEARNING[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 71-77 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1243

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