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In recent works by Yang et al. (2017a,b), and Yagli et al. (2019),geographical, temporal, and sequential deterministic reconciliation ofhierarchical photovoltaic (PV) power generation have been considered for asimulated PV dataset in California. In the first two cases, the reconciliationsare carried out in spatial and temporal domains separately. To further improveforecasting accuracy, in the third case these two reconciliation approaches aresequentially applied. During the replication of the forecasting experiment,some issues emerged about non-negativity and coherence (in space and/or intime) of the sequentially reconciled forecasts. Furthermore, while the accuracyimprovement of the considered approaches over the benchmark persistenceforecasts is clearly visible at any data granularity, we argue that an evenbetter performance may be obtained by a thorough exploitation of cross-temporalhierarchies. In this paper the cross-temporal point forecast reconciliationapproach is applied to generate non-negative, fully coherent (both in space andtime) forecasts. In particular, some relationships between two-step, iterativeand simultaneous cross-temporal reconciliation procedures are for the firsttime established, non-negativity issues of the final reconciled forecasts arecorrectly dealt with in a simple way, and the most recent cross-temporalreconciliation approaches are adopted. The normalised Root Mean Square Error isused to measure forecasting accuracy, and a statistical multiple comparisonprocedure is performed to rank the approaches. Besides assuring full coherence,and non-negativity of the reconciled forecasts, the results show that for theconsidered dataset, cross-temporal forecast reconciliation significantlyimproves on the sequential procedures proposed by Yagli et al. (2019), at anycross-sectional level of the hierarchy and for any temporal granularity.

Enhancements in cross-temporal forecast reconciliation, with an application to solar irradiance forecasts