Transforming point forecasts into probabilistic wind power scenarios using a hierarchical Bayesian model

Spatially aggregated power forecasts are commonly used to manage the energy grid; however, uncertainty estimates around those predictions are often unavailable. A forecast error, the difference between the predicted and the actual power output, offers valuable information for quantifying this uncertainty. Previous research has shown the benefit of using historical forecast errors to characterise uncertainty in power estimates. Building on this idea, we propose a hierarchical Bayesian formulation that transforms point forecasts into probabilistic scenarios. This approach incorporates temporal autocorrelation, seasonality, ramp behaviour, and heteroscedasticity, which are key for generating realistic power trajectories. Using wind power data from the United States at the aggregate level, and spatially disaggregated data from Scotland, the proposed approach produces calibrated probabilistic forecasts. For the United States data, the method improved wind power scenario sharpness compared with a nonparametric density-estimation benchmark. Our Bayesian hierarchical framework offers a flexible and interpretable alternative for uncertainty quantification in renewable energy forecasting that can be used in spatiotemporal settings.