Forecasting Italian daily electricity generation disaggregated by geographical zones and energy sources using coherent forecast combination

   Authors

Daniele Girolimetto, Tommaso Di Fonzo

   Published

February 17, 2025

   Publication details

In R. Castellano, G. De Luca, & E. Bruno (Eds.), Sustainability, innovation and digitalization: Statistical measurement for economic analysis - 3rd Italian Conference on Economic Statistics (pp. 169–172). Enzo Albano Edizioni

   Links
Abstract

A novel approach is applied for improving forecast accuracy and achieving coherence in forecasting the Italian daily energy generation time series. In hierarchical frameworks such as national energy generation disaggregated by geographical zones and energy sources, independently generated base forecasts often result in inconsistencies across the constraints. We deal with this issue through a coherent balanced multi-task forecast combination approach, which combines unbiased forecasts from multiple experts while ensuring coherence. Applied to the daily Italian electricity generation data, our method shows superior accuracy compared to single-task base and combined forecasts, and a state-of-the-art single-expert reconciliation technique, demonstrating to be an effective approach to forecasting linearly constrained multiple time series.