A machine learning model for hub-height short-term wind speed prediction
Zongwei Zhang, Lianlei Lin, Sheng Gao, Junkai Wang, Hanqing Zhao, Hangyi Yu
Abstract
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. Accurate short-term wind speed prediction is crucial for wind power systems. Authors propose a machine learning-based short-term wind prediction network using CNN-Transformer architecture, demonstrating strong transferability and robustness for predicting wind speeds in various climatic regions.