[论文解读] Uncertainty of short-term Wind Power Forecasts -- A methodology for on-line Assessment

Georges Kariniotakis, Pierre Pinson|arXiv (Cornell University)|Oct 4, 2023

Energy Load and Power Forecasting参考文献 7被引用 25

一句话总结

本文提出一种在线方法，使用用户定义的置信区间、针对风功率误差自适应重采样，以及两种气象风险指数 MRI 和 PRI 来信号预测风险。

ABSTRACT

The paper introduces a new methodology for assessing on-line the prediction risk of short-term wind power forecasts. The first part of this methodology consists in computing confidence intervals with a confidence level defined by the end-user. The resampling approach is used for this purpose since it permits to avoid a restrictive hypothesis on the distribution of the errors. It has been however appropriately adapted for the wind power prediction problem taking into account the dependency of the errors on the level of predicted power through appropriately defined fuzzy sets. The second part of the proposed methodology introduces two indices, named as MRI and PRI, that quantify the meteorological risk by measuring the spread of multi-scenario Numerical Weather Predictions and wind power predictions respectively. The multi-scenario forecasts considered here are based on the 'poor man's' ensembles approach. The two indices are used either to fine-tune the confidence intervals or to give signals to the operator on the prediction risk, i.e. the probabilities for the occurrence of high prediction errors depending on the weather stability. A relation between these indices and the level of prediction error is shown. Evaluation results over a three-year period on the case of a wind farm in Denmark and over a one-year period on the case of several farms in Ireland are given. The proposed methodology has an operational nature and can be applied to all kinds of wind power forecasting models

研究动机与目标

• 提供一种在线方法来量化短期风功率预测中的预测风险。
• 通过使用针对风功率误差定制的重采样方法，避免对误差分布作出严格的假设。
• 引入 MRI（来自多情景预测的气象风险）和 PRI（来自风功率预测的风险）指数，用以量化来自多情景预测和风功率预测的气象风险。

提出的方法

• 通过重采样在用户定义的置信水平下计算置信区间，以避免对分布的假设。
• 通过引入模糊集合来结合预测功率与误差之间的相关性，使重采样适应风功率。
• 定义两种指数，MRI（来自多情景预测的气象风险）和 PRI（来自风功率预测的风险），基于简易集合（poor man’s ensembles）的。
• 使用多情景预测（简易集合）来量化区间散布，并将其与预测误差水平相关联。
• 提供在天气稳定性条件下指示高预测误差概率的运营信号。

实验结果

研究问题

• RQ1 如何在不作出强分布假设的情况下构建短期风功率预测的在线置信区间？
• RQ2 多情景天气预测和风功率预测如何与较大预测误差的风险相关？
• RQ3 是否可以用两种指数（MRI 和 PRI）有效量化气象风险并指导运营决策？
• RQ4 天气稳定性、预测区间的散布与实际预测误差之间的关系是什么？

主要发现

• 置信区间可以在线使用重采样在用户定义的水平下计算，避免对严格分布假设。
• MRI 和 PRI 通过多情景天气预测的散布和风功率预测的散布来量化气象风险，分别。
• 这些指数可以根据天气稳定性微调置信区间，或提供高预测误差的运营风险信号。
• 观测到 MRI/PRI 值与预测误差水平之间存在关系。