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Weighted Autocorrelation based Prediction Interval Optimization for Wind Power Generation

机译：基于加权自相关的风力发电预测区间优化

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摘要

In this paper, an optimization methodology for the weighted autocorrelation based prediction interval is proposed and applied for the prediction of the wind power generation. The Coverage Width Based Criterion (CWC) is applied as the optimization criterion. The improved execution steps are as follows- At first, the string of 20 recent samples is autocorrelated with the similar strings of previous samples. Then, the samples next to the highest normalized correlation values and corresponding indexes are selected. After that, the amplitudes of the matched samples are adjusted by multiplying the value with the amplitude of recent string and by dividing by the amplitude of matched strings. These amplitude-adjusted samples are the prediction value for the next sample. Each prediction values are given a weight depending on the ratio of the amplitude of the string and the value of normalized correlation. The weight equation is trained with the CWC equation to find the optimum relation between amplitude and correlation values. The probability density distribution is derived from the weighted autocorrelation values. Finally, least relevant areas from corners are discarded to achieve the required coverage with smaller PI width. However, as the level of uncertainty changes over time, discarding historical percentile may result in a different coverage on later targets. Therefore, the percentage of discarding is also optimized with the CWC. Wind power generation is predicted and different weight equation and discarding percentages are achieved.
机译：本文提出了一种基于加权自相关的预测区间优化方法，并将其应用于风力发电量的预测。基于覆盖宽度的标准（CWC）被用作优化标准。改进的执行步骤如下：首先，将20个最近采样的字符串与先前采样的类似字符串自相关。然后，选择最高归一化相关值和相应索引旁边的样本。之后，通过将值与最近字符串的幅度相乘并除以匹配字符串的幅度，来调整匹配样本的幅度。这些幅度调整后的样本是下一个样本的预测值。根据字符串幅度与归一化相关值的比率，为每个预测值赋予权重。权重方程与CWC方程一起训练，以找到振幅和相关值之间的最佳关系。概率密度分布是从加权自相关值得出的。最后，舍弃角落中最不相关的区域，以实现所需的PI宽度较小的覆盖范围。但是，随着不确定性水平随时间变化，丢弃历史百分位可能会导致以后目标的覆盖范围有所不同。因此，使用CWC还可以优化丢弃百分比。预测了风力发电，并获得了不同的权重方程式和丢弃百分比。

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