Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China

Abstract

: This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province, China. The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing. At the same time, the wind speed predicted by the EC model is also included for comparative analysis. The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A. The CMA-GD model exhibits a monthly average correlation coefficient of 0.56, root mean square error of 2.72 m s –1 , and average absolute error of 2.11 m s –1 . In contrast, the EC model shows a monthly average correlation coefficient of 0.51, root mean square error of 2.83 m s –1 , and average absolute error of 2.21 m s –1 . Conversely, in Wind Farm B, the EC model outperforms the CMA-GD model. The CMA-GD model achieves a monthly average correlation coefficient of 0.55, root mean square error of 2.61 m s –1 , and average absolute error of 2.13 m s –1 . By contrast, the EC model displays a monthly average correlation coefficient of 0.63, root mean square error of 2.04 m s –1 , and average absolute error of 1.67 m s –1 .