Point and interval forecasting of electricity supply via pruned ensembles

This paper develops a new ensemble-based approach to point and interval forecasting, and focus on total electricity supply. The proposed approach combines Bootstrap Aggregation (Bagging), time series methods and a novel pruning routine that performs feature selection before the aggregation of forecasts. Monthly time series of the total electricity supplied between January 2000 and September 2020 in 16 countries are considered. Forecasting performance in different horizons is examined. As the data includes the COVID-19 pandemic that affected countries in different ways, with some visible changes in electricity demand, the likely impact of unusual observations on this proposal is also examined. A comparative, multi-step-ahead forecasting with out-of-sample evaluation is conducted using several forecasting accuracy metrics and detailed robustness checks. The results endorse the strength and resilience of the proposed approach in delivering not only accurate point forecasts, but also reliable prediction intervals under different economic settings. Moreover, the methodology presented herein is flexible, in the sense that it can be used to generate reliable point and interval forecasts for any time series in short and medium horizons.