风力发电综合密度预测及不确定性量化
Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation
摘要：
风力发电的不稳定性给实现安全电网运营带来了挑战。因此，有必要进行准确的风电功率预测及其不确定性量化。风电功率预测通常依赖于风速预测和风电转换过程。然而，目前大多数风电功率预测模型只考虑部分不确定性。本文提出了一个综合框架预测风功率密度，考虑风速预测和风功率转换过程中产生的不确定性。具体地说，我们使用非齐次几何布朗运动来模拟风速，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区间量化预测不确定性。为了预测功率输出，我们最小化预期的预测成本与（不等）惩罚的高估和低估。我们展示了所提出的方法的预测能力，使用来自位于不同地点的多个运营风电场的数据。
英文摘要：
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.
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