Machine Learning Models Generate High-Frequency Wind Vector Time Series for Energy and Safety Applications
Researchers have developed machine learning-based stochastic wind generators capable of producing realistic minute-scale wind vector time series using over 30 years of observational data from Lamont, Oklahoma. The models, built on time vector-quantized variational autoencoders, are designed to capture complex diurnal patterns in wind speed and direction. Accurate synthetic wind data could improve modeling for wind energy, wildfire spread, and aviation planning.
A new preprint posted to arXiv presents a suite of machine learning models for generating synthetic, high-frequency surface wind vector time series at the minute timescale. Drawing on more than 30 years of quality measurements from a site in Lamont, Oklahoma, and restricting analysis to June to reduce seasonal variation, the researchers employed time vector-quantized variational autoencoders as the core generative architecture. The models can generate a full day of wind data at once or conditionally based on the prior day's winds, and they also incorporate a discrete weather state variable to improve realism. Evaluation using both formal and informal methods found that the best models successfully replicate diurnal changes in wind volatility but fall short in reproducing the observed distribution of extreme wind speeds. The authors note that the complex diurnal structures in the data pose challenges that standard time series models would struggle to address, motivating the machine learning approach. Such generators have practical downstream applications in wind energy forecasting, wildfire behavior modeling, and aviation safety assessment.
What's missing
The study is limited to a single geographic site (Lamont, Oklahoma) and a single month (June), leaving open questions about generalizability across different climates, seasons, and terrain types. The failure to accurately reproduce extreme wind speed distributions is acknowledged but not fully explained, and it remains unclear whether architectural changes or more data could resolve this limitation.
What different sources said
- arXiv cs.LGCenter
Stochastic weather generators for high-frequency wind vector time series
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