Forecasting Italian daily electricity generation disaggregated by geographical zones and energy sources using coherent forecast combination
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915155344883712 |
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| author | Girolimetto, Daniele Di Fonzo, Tommaso |
| author_facet | Girolimetto, Daniele Di Fonzo, Tommaso |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11878 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting Italian daily electricity generation disaggregated by geographical zones and energy sources using coherent forecast combination Girolimetto, Daniele Di Fonzo, Tommaso Applications Methodology 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. |
| title | Forecasting Italian daily electricity generation disaggregated by geographical zones and energy sources using coherent forecast combination |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2502.11878 |