Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation

TL;DR

This paper introduces an integrative framework for wind power density prediction that accounts for uncertainties in wind speed forecasting and the wind-to-power conversion process, enabling more accurate and reliable power forecasts.

Contribution

It develops a novel approach modeling wind speed with inhomogeneous Geometric Brownian Motion and derives a closed-form wind power density prediction with uncertainty quantification.

Findings

Effective uncertainty quantification through prediction intervals

Improved wind power forecasting accuracy across multiple sites

Minimized expected prediction cost with asymmetric penalties

Abstract

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