A sudden shift in wind behavior throws off most estimates, making steady forecasts tough. When the breeze changes without warning, keeping lights on gets complicated. Old-style models struggle because gusts do not follow straight lines or simple rules. A fresh method pops up here - machine learning steps in to guess how much electricity wind turbines will make, fed by weather numbers and details about the machines themselves. Instead of one-size-fits-all formulas, tools like Random Forest twist through complex tangles while Gradient Boosting climbs error trails; meanwhile, LSTM keeps an eye on time sequences that unfold slowly. Each model dances differently with the data, yet they all aim at the same target: better forecasts without relying on old assumptions.
Ravindra et al. (2026) studied this question.