论文标题
跨时期预测对帐的增强，并应用太阳辐照度预测
Enhancements in cross-temporal forecast reconciliation, with an application to solar irradiance forecasts
论文作者
论文摘要
在Yang等人的最新作品中。 （2017a，b）和Yagli等。 （2019年），在加利福尼亚州的模拟PV数据集中，已经考虑了分层光伏（PV）发电的地理，时间和顺序确定性对帐。在前两种情况下，对帐是在空间和时间域中分别进行的。为了进一步提高预测准确性，在第三种情况下，这两种对帐方法被顺序应用。在复制预测实验期间，关于依次调和预测的非阴性和连贯性（在空间和/或时间）中出现了一些问题。此外，虽然在任何数据粒度性下，清楚地看到了对基准持久性预测的精确提高，但我们认为，通过彻底利用跨暂时性层次结构，可以获得更好的性能。在本文中，应用跨性别点的预测对帐方法用于生成非负，完全连贯的（在时空和时间上）的预测。特别是，两步，迭代和同时进行跨时空的对帐程序之间的某些关系首次建立，最终和解预测的非负问题是以一种简单的方式正确处理的，并且采用了最新的跨日期和解方法。归一化均方根误差用于测量预测精度，并执行统计多重比较程序以对方法进行排名。除了确保对核对预测的完全连贯性和非负性外，结果表明，对于被考虑的数据集，跨时空预测对帐在Yagli等人提出的顺序程序上都显着改善。 （2019年），在层次结构的任何横截面级别和任何时间粒度。
In recent works by Yang et al. (2017a,b), and Yagli et al. (2019), geographical, temporal, and sequential deterministic reconciliation of hierarchical photovoltaic (PV) power generation have been considered for a simulated PV dataset in California. In the first two cases, the reconciliations are carried out in spatial and temporal domains separately. To further improve forecasting accuracy, in the third case these two reconciliation approaches are sequentially applied. During the replication of the forecasting experiment, some issues emerged about non-negativity and coherence (in space and/or in time) of the sequentially reconciled forecasts. Furthermore, while the accuracy improvement of the considered approaches over the benchmark persistence forecasts is clearly visible at any data granularity, we argue that an even better performance may be obtained by a thorough exploitation of cross-temporal hierarchies. In this paper the cross-temporal point forecast reconciliation approach is applied to generate non-negative, fully coherent (both in space and time) forecasts. In particular, some relationships between two-step, iterative and simultaneous cross-temporal reconciliation procedures are for the first time established, non-negativity issues of the final reconciled forecasts are correctly dealt with in a simple way, and the most recent cross-temporal reconciliation approaches are adopted. The normalised Root Mean Square Error is used to measure forecasting accuracy, and a statistical multiple comparison procedure is performed to rank the approaches. Besides assuring full coherence, and non-negativity of the reconciled forecasts, the results show that for the considered dataset, cross-temporal forecast reconciliation significantly improves on the sequential procedures proposed by Yagli et al. (2019), at any cross-sectional level of the hierarchy and for any temporal granularity.
