Day-Ahead Electricity Price Forecasting Using Merit-Order Curves Time Series
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914243393093632 |
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| author | Koechlin, Guillaume Bovera, Filippo Secchi, Piercesare |
| author_facet | Koechlin, Guillaume Bovera, Filippo Secchi, Piercesare |
| contents | We introduce a general, simple, and computationally efficient framework for predicting day-ahead supply and demand merit-order curves, from which both point and probabilistic electricity price forecasts can be derived. We conduct a rigorous empirical comparison of price forecasting performance between the proposed curve-based model, i.e., derived from predicted merit-order curves, and state-of-the-art price-based models that directly forecast the clearing price, using data from the Italian day-ahead market over the 2023-2024 period. Our results show that the proposed curve-based approach significantly improves both point and probabilistic price forecasting accuracy relative to price-based approaches, with average gains of approximately 5%, and improvements of up to 10% during mid-day hours, when prices occasionally drop due to high renewable generation and low demand. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17758 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Day-Ahead Electricity Price Forecasting Using Merit-Order Curves Time Series Koechlin, Guillaume Bovera, Filippo Secchi, Piercesare Applications We introduce a general, simple, and computationally efficient framework for predicting day-ahead supply and demand merit-order curves, from which both point and probabilistic electricity price forecasts can be derived. We conduct a rigorous empirical comparison of price forecasting performance between the proposed curve-based model, i.e., derived from predicted merit-order curves, and state-of-the-art price-based models that directly forecast the clearing price, using data from the Italian day-ahead market over the 2023-2024 period. Our results show that the proposed curve-based approach significantly improves both point and probabilistic price forecasting accuracy relative to price-based approaches, with average gains of approximately 5%, and improvements of up to 10% during mid-day hours, when prices occasionally drop due to high renewable generation and low demand. |
| title | Day-Ahead Electricity Price Forecasting Using Merit-Order Curves Time Series |
| topic | Applications |
| url | https://arxiv.org/abs/2512.17758 |