The volatile nature of wind power generation creates challenges in achieving secure power grid operations. It is, therefore, necessary to accurately predict wind power 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.

中文翻译：

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风力发电的综合密度预测与不确定性量化

风力发电的波动性给实现安全的电网运行带来了挑战。因此，有必要准确预测风电功率及其不确定性量化。风电功率预测通常取决于风速预测和风电转换过程。然而，目前大多数风电预测模型只考虑了部分不确定性。本文开发了一个用于预测风功率密度的综合框架，同时考虑了风速预测和风电转换过程中产生的不确定性。具体来说，我们使用非均匀几何布朗运动对风速进行建模，并将风速预测密度转换为封闭形式的风功率密度。由此产生的风功率密度允许通过预测间隔量化预测的不确定性。为了预测功率输出，我们通过对高估和低估的（不等）惩罚来最小化预期预测成本。我们使用来自位于不同站点的多个运行中的风电场的数据展示了所提出方法的预测能力。