A novel reconciliation approach for hierarchical electricity consumption forecasting based on resistant regression

Power systems have become increasingly complex throughout the years, with divisions that may vary across classes of consumption and geographic regions. Hence, accurate demand forecasts across sub-national levels have grown in importance for decision making. Official planning reports and most forecasting methods, however, have been tailored to deliver consumption forecasts only at the most aggregate level, i.e., total energy consumption. This work proposes a hierarchical forecast reconciliation approach aimed at delivering accurate forecasts of energy demand across all divisions in a power system. Its novelty relies on the use of resistant-based estimators to aid in the process of forecast reconciliation, so that outliers and influential points that may be present in the base forecasts have minimal or even null effects on reconciliation weights. Data from the Brazilian National Interlinked System are considered in multiple experiments that compare the performance of the proposed approach with those from traditional and state-of-the-art hierarchical forecasting methods. Overall, the proposed reconciliation approach shows promising forecasting results under multiple settings and through the lens of several evaluation metrics. The average gains brought forth by the proposed methodology, in terms of accumulated energy that can be 'saved' across all divisions of the power system given more accurate forecasts, is greater than 900,000 MWh for forecast horizons of three and four months ahead. This is particularly important for decision-making in the energy industry, since the more accurate the forecasts are, the easier it is to plan and dispatch, thus the method has potential for real savings in the sector. Furthermore, the developed methodology is flexible, as it can be readily applied to other sets of hierarchical time series. Findings and policy implications are further discussed.