Energy load forecasting using Terna public data: a free lunch multi-task combination approach
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916618057023488 |
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| author | Girolimetto, Daniele Di Fonzo, Tommaso |
| author_facet | Girolimetto, Daniele Di Fonzo, Tommaso |
| contents | We propose a quick-and-simple procedure to augment the accuracy of 15-minutes Italian load forecasts disaggregated by bidding zones published by Terna, the operator of the Italian electricity system. We show that a stacked-regression multi-task combination approach using Terna and daily random walk naive forecasts, is able to produce significantly more accurate forecasts immediately after Terna publishes on its data portal the energy load measurements for the previous day, and the forecasts for the current day. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energy load forecasting using Terna public data: a free lunch multi-task combination approach Girolimetto, Daniele Di Fonzo, Tommaso Applications Methodology We propose a quick-and-simple procedure to augment the accuracy of 15-minutes Italian load forecasts disaggregated by bidding zones published by Terna, the operator of the Italian electricity system. We show that a stacked-regression multi-task combination approach using Terna and daily random walk naive forecasts, is able to produce significantly more accurate forecasts immediately after Terna publishes on its data portal the energy load measurements for the previous day, and the forecasts for the current day. |
| title | Energy load forecasting using Terna public data: a free lunch multi-task combination approach |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2502.11873 |