arXiv:2605.12196v2 Announce Type: replace Abstract: Accurate ultra-short-term wind power forecasting is critical for grid dispatch and reserve management, yet remains challenging due to the non-stationary, condition-dependent nature of wind generation. Meteorological exogenous variables carry substantial predictive information, but the most informative variable combination varies across sites, operating conditions, and prediction horizons. Existing deep learning approaches either treat exogenous inputs as generic auxiliary channels through uniform mixing or soft gating, or rely on fixed prepro
The increasing integration of renewable energy sources like wind power necessitates more accurate forecasting methods for grid stability, highlighting the ongoing R&D in AI for energy management.
Improved ultra-short-term wind power forecasting directly enhances grid reliability and efficiency, reducing the need for costly reserve power and enabling greater renewable energy penetration.
The development of more sophisticated AI models that intelligently select and utilize exogenous meteorological data is leading to more precise and adaptable wind power predictions.
- · Renewable energy operators
- · Grid management companies
- · AI/ML researchers in energy
- · Fossil fuel power generators (marginally)
- · Less advanced forecasting providers
More stable and cost-effective integration of wind power into national grids.
Accelerated investment and deployment of wind energy projects due to enhanced predictability and reduced operational risks.
Potential for refined energy trading strategies based on highly accurate, real-time renewable energy generation forecasts, reshaping energy markets.
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Read at arXiv cs.LG