mlsanityData to 7 Oct 2026

WindDragon: Enhancing wind power forecasting with Automated Deep Learning

arXiv 2402.14385 ↗First posted 22 Feb 2024cs.LG physics.ao-ph stat.MLcited by 3

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Abstract (arXiv)

Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators due to its variability and uncertainty. Therefore, accurate forecasting of wind power is critical for grid operation and system balancing. This paper presents an innovative approach to short-term (1 to 6 hour horizon) windpower forecasting at a national level. The method leverages Automated Deep Learning combined with Numerical Weather Predictions wind speed maps to accurately forecast wind power.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 22 Feb 2024.

Builds on

11 of 23 references.

References · 11

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Towards Accurate Forecasting of Renewable Energy : Building Datasets and Benchmarking Machine Learning Models for Solar and Wind Power in Francecited by 1
  2. 2025Weather Maps as Tokens: Transformers for Renewable Energy Forecastingcited by 0
  3. 2026UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routingcited by 0