Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation

The volatile nature of wind power generation creates challenges in achieving secure grid operations. It is, therefore, necessary to accurately predict and its uncertainty quantification. Wind forecasting usually depends on speed prediction the wind-to-power conversion process. However, most current models only consider portions uncertainty. This paper develops an integrative framework for predicting density, considering uncertainties arising from both Specifically, we model using inhomogeneous Geometric Brownian Motion convert density into a closed-form. resulting allows quantifying through intervals. To forecast output, minimize expected cost with (unequal) penalties overestimation underestimation. We show predictive proposed approach data multiple operating farms located at different sites.