New AI framework integrates weather and turbine data for wind power forecasting

Researchers have developed a new multimodal framework for short-term wind power forecasting that integrates SCADA data from wind turbines with numerical weather prediction (NWP) forecasts. This approach addresses the challenge of combining heterogeneous data types by decomposing inputs into scalar and vector features and using a geometric encoder for rotation-invariant features. The framework employs a Fourier Neural Operator (FNO) architecture to model long-range spatiotemporal relationships, demonstrating superior performance over existing methods in experiments on three real-world wind farms. AI

IMPACT This research could improve the accuracy of wind power forecasting, aiding grid stability and operational planning.

RANK_REASON The cluster contains an academic paper detailing a new AI model architecture and its application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Fourier Neural Operator
• Fourier Neural Operators
• numerical weather prediction
• SCADA

• paper
• model release

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