A machine learning model for hub-height short-term wind speed prediction.

Area of Science:

• Renewable Energy Systems
• Meteorological Forecasting
• Computational Fluid Dynamics

Background:

• Accurate short-term wind speed prediction is essential for the safe, stable, and efficient operation of wind power systems.
• Existing models often struggle with fine-grid vector wind speed prediction, particularly in complex geographical regions.

Purpose of the Study:

• To develop and evaluate a novel multivariate meteorological data fusion wind prediction network (MFWPN).
• To assess the MFWPN's performance in fine-grid vector wind speed prediction for Northeast China.
• To demonstrate the model's generalization capabilities and computational efficiency.

Main Methods:

• The study proposes a multivariate meteorological data fusion wind prediction network (MFWPN).
• The MFWPN integrates diverse meteorological data for enhanced prediction accuracy.
• Performance was evaluated against the ECMWF-HRES model and through transfer and efficiency experiments.

Main Results:

• The MFWPN significantly outperforms the ECMWF-HRES model in vector wind speed prediction accuracy within the initial 6 hours.
• Transfer experiments confirmed the MFWPN's robust generalized performance, enabling rapid offsite prediction.
• Efficiency experiments revealed the MFWPN's capability to predict vector wind speeds on a 24-hour fine grid in just 18 milliseconds.

Conclusions:

• The MFWPN is a highly accurate and efficient tool for ultrashort- and short-term wind speed forecasting.
• Its ability to handle fine-grid vector wind speed prediction makes it suitable for large regional wind centers.
• The model facilitates improved deployment planning for wind power generation.