A conditional probabilistic framework using monthly Weibull parameters forecasted by Kalman filter on VAR(1), three Weibull-stationary SDE models, and XGBoost power curve mapping achieves CRPS of 1.57 m/s and low Wasserstein distances on real turbine data, preferring the diffusion-first model for sp
Leveraging stochastic differential equations for probabilistic forecasting of wind power using a dynamic power curve
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Weibull-Stationary Stochastic Differential Equations for Conditional Long-Horizon Wind Power Forecasting
A conditional probabilistic framework using monthly Weibull parameters forecasted by Kalman filter on VAR(1), three Weibull-stationary SDE models, and XGBoost power curve mapping achieves CRPS of 1.57 m/s and low Wasserstein distances on real turbine data, preferring the diffusion-first model for sp