arXiv:2607.01670v1 Announce Type: new Abstract: Day-ahead wind power forecasting is essential for cost-effective power-system operation. It is primarily driven by future meteorological conditions while retaining temporal dependencies in power generation. In practice, observed wind-farm power often entangles physically available power with local environmental effects and latent operational states, such as shutdowns and curtailment. Existing physical models provide useful constraints but adapt poorly across wind farms, whereas data-driven models can capture rich correlations but often conflate m
The increasing integration of renewable energy sources, particularly wind power, necessitates more accurate forecasting models to ensure grid stability and cost-effective operations.
Improved day-ahead wind power forecasting has direct economic benefits for energy providers and grid operators, reducing uncertainty and optimizing resource allocation.
This development offers a more unified and potentially accurate approach to wind power forecasting, combining physical understanding with data-driven methods to overcome limitations of previous models.
- · Renewable energy companies
- · Power grid operators
- · AI/ML research sector
- · Energy trading firms
- · Traditional forecasting methodologies
- · Fossil fuel power generators
More reliable wind energy integration into national grids.
Reduced need for expensive peaker plants and grid stabilization measures for wind power.
Accelerated decarbonization efforts through higher confidence in renewable energy supply.
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Read at arXiv cs.LG