Zhang Zongwei, Lin Lianlei, Gao Sheng, Wang Junkai, Zhao Hanqing, Yu Hangyi
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Technological Innovation Center of Littoral Test, Harbin, China.
Nat Commun. 2025 Apr 3;16(1):3195. doi: 10.1038/s41467-025-58456-4.
Accurate short-term wind speed prediction is crucial for maintaining the safe, stable, and efficient operation of wind power systems. We propose a multivariate meteorological data fusion wind prediction network (MFWPN) to study fine-grid vector wind speed prediction, taking Northeast China as an example. Results show that MFWPN outperforms the ECMWF-HRES model regarding vector wind speed prediction accuracy within the first 6 h. Transfer experiments demonstrate the good generalized performance of the MFWPN, which can be quickly applied to offsite prediction. Efficiency experiments show that the MFWPN takes only 18 ms to predict vector wind speeds on a 24-hour fine grid over the future northeastern region. With its demonstrated accuracy and efficiency, the MFWPN can be an effective tool for predicting vector wind speeds in large regional wind centers and can help in ultrashort- and short-term deployment planning for wind power.
准确的短期风速预测对于维持风力发电系统的安全、稳定和高效运行至关重要。我们提出了一种多变量气象数据融合风力预测网络（MFWPN），以中国东北地区为例研究精细网格矢量风速预测。结果表明，在矢量风速预测精度方面，MFWPN在前6小时内优于欧洲中期天气预报中心高分辨率集合预报（ECMWF-HRES）模型。迁移实验证明了MFWPN具有良好的泛化性能，能够快速应用于场外预测。效率实验表明，MFWPN预测未来东北地区24小时精细网格上的矢量风速仅需18毫秒。凭借其已证明的准确性和效率，MFWPN可以成为大型区域风电场矢量风速预测的有效工具，并有助于风力发电的超短期和短期部署规划。
