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Forecast uncertainty-based performance degradation diagnosis of solar PV systems

机译：预测太阳能光伏系统的不确定性性能下降诊断

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摘要

In this study, the authors are interested in estimating how much a PV system underperforms than expected by exploiting forecast uncertainty. For this, they first study a forecast accuracy-related forecast uncertainty metric using the ensemble method based on the dropout technique, which is widely used in deep learning forecasting models. Given the forecast accuracy-related uncertainty metric, the rationale of the authors' approach is that forecast accuracy is likely to decrease compared to the normal case of similar uncertainty metric values if any performance degradation happens. It is because similar uncertainty metric values are likely to show similar forecast accuracy. Therefore, they generate a standard table by simulating possible performance degradation cases and conduct the performance degradation diagnosis by looking up the standard table based on the uncertainty metric. From the experiments, in the case of persistent degradation, they show that their approach estimates the performance degradation with the estimation error of around 1% while an uncertainty-unaware approach shows the estimation error of up to 5%. In the case of temporal degradation, their approach shows the estimation error of around 3%, while the uncertainty-unaware approach does not show meaningful result.
机译：在这项研究中，作者有兴趣估计PV系统的表现低于预期，通过利用预测不确定性。为此，他们首先使用基于辍学技术的集合方法研究预测精度相关的预测不确定性度量，这广泛用于深度学习预测模型。鉴于预测与预测的准确性不确定性度量，作者方法的基本原理是，如果发生任何性能下降，则预测精度可能会减少与类似的不确定性度量值的正常情况相比。这是因为类似的不确定性度量值可能显示出类似的预测准确性。因此，它们通过模拟可能的性能劣化案例来生成标准表，并通过基于不确定性度量来查找标准表来进行性能下降诊断。从实验中，在持续降级的情况下，他们表明它们的方法估计了估计误差约为1％的性能下降，而不确定的不知道方法显示估计误差高达5％。在时间劣化的情况下，它们的方法显示估计误差约为3％，而不确定性 - 不知道方法不会显示有意义的结果。

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