Abstract

Forecast reconciliation has been widely studied in cross-sectional and temporal hierarchies, but its role in cross-temporal settings for photovoltaic (PV) forecasting remains insufficiently examined. In particular, the relative benefits of reconciliation across heterogeneous forecasting approaches, including statistical, machine learning, deep learning, and foundation models, have not been clearly established. This study addresses that gap by evaluating direct, univariate, and iterative cross-temporal reconciliation strategies applied to TBATS, LightGBM, KAN, NBEATSx, NHITS, and TimeGPT using Belgian PV generation data from 2020 to 2025 across weekly, daily, and hourly frequencies and national, regional, and provincial levels. Model efficacy is assessed through 52-week walk-forward cross-validation, which provides a full-year coverage. Under the fixed-configuration experimental protocol adopted in this study, the results show that the gains from reconciliation vary substantially across forecasting families. LightGBM achieved the largest observed gains, with its univariate and iterative schemes achieving global error reductions of up to 19.6% relative to the Bottom-Up benchmark. KAN, NHITS, and NBEATSx also benefited from reconciliation, with their best reconciled variants yielding reductions of up to 11.9%. TimeGPT and TBATS achieved reductions of up to 9.2% and 14.5%, respectively, although their global errors were higher than those obtained by the best machine learning and deep learning configurations in this evaluation. Across the fixed baseline configurations considered here, LightGBM obtained the lowest global errors before and after reconciliation. These findings show that cross-temporal reconciliation can be an effective post-processing strategy, but its impact depends strongly on the underlying base forecasting model. Therefore, the observed advantage of LightGBM should be interpreted as conditional on the adopted feature set, implementations, and baseline configurations.

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