On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929380039589888 |
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| author | Brusaferri, Alessandro Ballarino, Andrea Grossi, Luigi Laurini, Fabrizio |
| author_facet | Brusaferri, Alessandro Ballarino, Andrea Grossi, Luigi Laurini, Fabrizio |
| contents | Probabilistic electricity price forecasting (PEPF) is subject of increasing interest, following the demand for proper quantification of prediction uncertainty, to support the operation in complex power markets with increasing share of renewable generation. Distributional neural networks ensembles have been recently shown to outperform state of the art PEPF benchmarks. Still, they require critical reliability enhancements, as fail to pass the coverage tests at various steps on the prediction horizon. In this work, we propose a novel approach to PEPF, extending the state of the art neural networks ensembles based methods through conformal inference based techniques, deployed within an on-line recalibration procedure. Experiments have been conducted on multiple market regions, achieving day-ahead forecasts with improved hourly coverage and stable probabilistic scores. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02722 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices Brusaferri, Alessandro Ballarino, Andrea Grossi, Luigi Laurini, Fabrizio Machine Learning Probabilistic electricity price forecasting (PEPF) is subject of increasing interest, following the demand for proper quantification of prediction uncertainty, to support the operation in complex power markets with increasing share of renewable generation. Distributional neural networks ensembles have been recently shown to outperform state of the art PEPF benchmarks. Still, they require critical reliability enhancements, as fail to pass the coverage tests at various steps on the prediction horizon. In this work, we propose a novel approach to PEPF, extending the state of the art neural networks ensembles based methods through conformal inference based techniques, deployed within an on-line recalibration procedure. Experiments have been conducted on multiple market regions, achieving day-ahead forecasts with improved hourly coverage and stable probabilistic scores. |
| title | On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.02722 |