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.
翻译:在最近Yang等人(2017a,b)和Yagli等人(2019年)的著作中,考虑在加利福尼亚州模拟光电池数据集中模拟光电池发电的地理、时间和顺序调节(2017a,b)和Yagli等人(2019年)进行了地理、时间和顺序调节。在前两个案例中,对空间和时间的调节分别进行。为了进一步提高预测准确性,在第三个案例中,对这两种调节方法相继适用。在复制预测实验期间,出现了一些关于(空间和(或)时间)连续调节预测的不增强性和一致性的问题。此外,虽然在任何数据颗粒上,都明显可见考虑过的时间光电光电光电发电方法的准确性改善基准持久性预测的准确性。在前两个案例中,对空间和时间的平衡性进行了分别的调节。 跨时点预测用于产生非负面的预测性、(在空间和时间上)完全一致的预测。 跨级、跨级、跨级、跨级、跨级、跨级的预测程序之间的某些关系是最近确定的、不精确性、不准确的对等的预测。