Wind power prediction method based on cross-modal space-time attention fusion
By combining Transformer and GAT models with a cross-modal spatiotemporal attention fusion network, the problem of incomplete spatiotemporal feature capture in wind power prediction is solved, achieving more efficient wind power prediction accuracy and adaptability.
CN121745573APending Publication Date: 2026-03-27GUANGDONG UNIV OF TECH
1 Cites 1 Cited by
Patent Information
- Application Number
- CN202511889397.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-15
- Publication Date
- 2026-03-27
AI Technical Summary
Technical Problem
Existing wind power prediction technologies are incomplete in capturing spatiotemporal features, have low feature fusion efficiency, and cannot adapt to changes in the importance of spatiotemporal features under different weather scenarios.
Method used
The Transformer model is used to extract temporal features, the GAT model is used to extract spatial features, and a cross-modal spatiotemporal attention fusion network is used to perform dual-scale feature fusion to adapt to wind power prediction under different weather scenarios.
Benefits of technology
It improves the accuracy and adaptability of wind power forecasting, enhances feature quality and fusion effect, and adapts to wind power forecasting under different weather scenarios.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to a wind power prediction method based on cross-modal space-time attention fusion. Accurate wind power prediction in different weather scenes is realized. The method comprises the following steps: preprocessing wind power numerical weather forecast data to obtain preprocessed data; performing feature extraction on the preprocessed data by adopting a Transform model to obtain a time feature; performing feature extraction on the preprocessed data by adopting a GAT model to obtain spatial features; performing dual-scale feature fusion on the time feature and the spatial feature by adopting a preset neural network to obtain a fused feature; and carrying out standardization processing on the fusion features to obtain standard wind power data.
Need to check novelty before this filing date? Find Prior Art