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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.04405 |
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| _version_ | 1866910605067157504 |
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| author | Pernigo, Luca Sen, Rohan Baroli, Davide |
| author_facet | Pernigo, Luca Sen, Rohan Baroli, Davide |
| contents | Accurate energy demand forecasting is crucial for sustainable and resilient energy development. To meet the Net Zero Representative Concentration Pathways (RCP) $4.5$ scenario in the DACH countries, increased renewable energy production, energy storage, and reduced commercial building consumption are needed. This scenario's success depends on hydroelectric capacity and climatic factors. Informed decisions require quantifying uncertainty in forecasts. This study explores a non-parametric method based on \emph{reproducing kernel Hilbert spaces (RKHS)}, known as kernel quantile regression, for energy prediction. Our experiments demonstrate its reliability and sharpness, and we benchmark it against state-of-the-art methods in load and price forecasting for the DACH region. We offer our implementation in conjunction with additional scripts to ensure the reproducibility of our research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces Pernigo, Luca Sen, Rohan Baroli, Davide Machine Learning Artificial Intelligence Systems and Control I.2; G.4 Accurate energy demand forecasting is crucial for sustainable and resilient energy development. To meet the Net Zero Representative Concentration Pathways (RCP) $4.5$ scenario in the DACH countries, increased renewable energy production, energy storage, and reduced commercial building consumption are needed. This scenario's success depends on hydroelectric capacity and climatic factors. Informed decisions require quantifying uncertainty in forecasts. This study explores a non-parametric method based on \emph{reproducing kernel Hilbert spaces (RKHS)}, known as kernel quantile regression, for energy prediction. Our experiments demonstrate its reliability and sharpness, and we benchmark it against state-of-the-art methods in load and price forecasting for the DACH region. We offer our implementation in conjunction with additional scripts to ensure the reproducibility of our research. |
| title | Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces |
| topic | Machine Learning Artificial Intelligence Systems and Control I.2; G.4 |
| url | https://arxiv.org/abs/2408.04405 |