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Computing, Environment and Life Sciences

AI-Generated Bounded Uncertainty Wind Forecasts

Argonne is applying machine learning techniques to obtain probabilistic forecasts of wind.

As the use of renewable energy technologies grows, probabilistic forecasting of a wind power generation is needed to help ensure reliable and economic power systems operation.

Argonne is using a normalizing flow deep-learning model to learn the joint distribution between current wind conditions” and 3-hours future wind conditions.”

We use this model to construct the forecast distribution P(“3-Hours Future” | Current”). One distinguishing feature of this approach is that forecast distributions are much more informative than forecast point-estimates, and more useful for risk management. Most deep-learning forecasting applications yield point estimates.