arXiv:2606.02849v1 Announce Type: new Abstract: Interval wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it accounts for the inherent uncertainty of wind resources. This study presents a systematic literature review focused on hybrid approaches to interval forecasting of wind generation, exploring the combination of deep learning, modal decomposition, and statistical methods. To guide the paper selection, Latent Dirichlet Allocation (LDA) was applied for topic modeling, enabling the identification of patterns and research trends. The find
The increasing complexity and scale of wind energy integration necessitate more sophisticated forecasting models to manage inherent uncertainties, driven by global renewable energy targets.
Improved wind power forecasting directly enhances grid stability, reduces curtailment, and optimizes energy markets, making renewable integration more efficient and cost-effective.
The adoption of advanced hybrid approaches combining deep learning, modal decomposition, and statistical methods will lead to more accurate and reliable wind power predictions.
- · Renewable energy developers
- · Grid operators
- · AI/Machine Learning companies
- · Energy trading firms
- · Traditional fossil fuel generators (indirectly)
- · Less efficient grid management systems
Increased confidence in wind energy as a reliable power source, accelerating its deployment.
Reduced need for expensive spinning reserves and backup power, lowering overall electricity costs.
Enhanced ability to balance intermittent renewable generation could enable more ambitious clean energy policies and grid modernization efforts.
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Read at arXiv cs.LG