Accurate electricity consumption forecasting is crucial for decision-makers in the energy industry, who aim at balancing risk reduction and socioeconomic development. By constructing multiple Hierarchical Time Series (HTS) structures that can represent the energy consumption flows within a power system and employing a comprehensive set of forecast reconciliation methods, we seek to determine which forecasting approach would provide the most accurate estimates for monthly energy consumption in the Brazilian National Interconnected System (SIN). An empirical assessment using monthly time series of electricity demand across the Brazilian SIN spanning from January 2004 to December 2022 is considered. While the findings reveal significant variations in the effectiveness of reconciliation techniques across diverse hierarchical structures of the same aggregated series, consistent improvements in forecast accuracy are observed when employing robust reconciliation techniques. Such improvements are particularly noteworthy in hierarchical time series structures where regional divisions constitute the intermediate level. The findings underscore the pivotal role of reconciliation methods in enhancing forecast accuracy across multiple hierarchical structures of energy consumption series. Subsequent discussions address the implications of these findings for decision-making processes and offer recommendations for future research directions.
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