Machine Learning Models Generate High-Frequency Wind Vector Time Series for Energy and Safety Applications
Researchers developed machine learning models based on 30+ years of minute-scale wind measurements from Oklahoma to generate realistic synthetic wind time series. The models use vector-quantized variational autoencoders and can capture complex daily wind patterns, with potential applications in wind energy, wildfire prediction, and aviation. The generators successfully reproduce diurnal wind volatility patterns but have limitations in matching extreme wind speed distributions.
A new study presents stochastic weather generators capable of producing high-frequency surface wind vector time series using machine learning trained on over 30 years of quality measurements from Lamont, Oklahoma. The researchers focused on June data to minimize seasonal variation and developed multiple approaches using vector-quantized variational autoencoders, including models that generate single days of data and models conditioned on previous-day winds. The generators can incorporate discrete weather state variables and were evaluated using both formal statistical methods and informal assessments. While the best-performing models successfully capture complex diurnal structures in wind speed and direction—features that would be difficult for traditional time series models—they show limitations in accurately reproducing the observed distribution of extreme wind events. These synthetic wind generators have broad applications across wind energy optimization, wildfire spread modeling, and aviation safety.
What's missing
The study does not discuss computational requirements or runtime performance of the different generator approaches, nor does it provide specific quantitative metrics comparing model performance against baseline methods. Additionally, the generalizability of models trained on a single Oklahoma site to other geographic locations or seasons beyond June is not addressed.
What different sources said
- arXiv cs.LGCenter
Stochastic weather generators for high-frequency wind vector time series
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