Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914511236104192 |
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| author | Kleinebrahm, Max Berrisch, Jonathan Eiser, Philipp Fichtner, Wolf Hagenmeyer, Veit Hertel, Matthias Koster, Nils Lerch, Sebastian Mikut, Ralf Priesmann, Jan Schienle, Melanie Schaefer, Benjamin Weinand, Jann Ziel, Florian |
| author_facet | Kleinebrahm, Max Berrisch, Jonathan Eiser, Philipp Fichtner, Wolf Hagenmeyer, Veit Hertel, Matthias Koster, Nils Lerch, Sebastian Mikut, Ralf Priesmann, Jan Schienle, Melanie Schaefer, Benjamin Weinand, Jann Ziel, Florian |
| contents | Energy forecasting research faces a persistent comparability gap that makes it difficult to measure consistent progress over time. Reported accuracy gains are often not directly comparable because models are evaluated under study-specific datasets, time periods, information sets, and scoring setups, while widely used benchmarks and competition datasets are typically tied to fixed historical windows. This paper introduces the Energy-Arena, a dynamic benchmarking platform for operational energy time series forecasting that provides a continuously updated reference point as energy systems evolve. The platform operates as an open, API-based submission system and standardizes challenge definitions and submission deadlines aligned with operational constraints. Performance is reported on rolling evaluation windows via persistent leaderboards. By moving from retrospective backtesting to forward-looking benchmarking, the Energy-Arena enforces standardized ex-ante submission and ex-post evaluation, thereby improving transparency by preventing information leakage and retroactive tuning. The platform is publicly available at Energy-Arena.org. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24705 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting Kleinebrahm, Max Berrisch, Jonathan Eiser, Philipp Fichtner, Wolf Hagenmeyer, Veit Hertel, Matthias Koster, Nils Lerch, Sebastian Mikut, Ralf Priesmann, Jan Schienle, Melanie Schaefer, Benjamin Weinand, Jann Ziel, Florian Econometrics Machine Learning Energy forecasting research faces a persistent comparability gap that makes it difficult to measure consistent progress over time. Reported accuracy gains are often not directly comparable because models are evaluated under study-specific datasets, time periods, information sets, and scoring setups, while widely used benchmarks and competition datasets are typically tied to fixed historical windows. This paper introduces the Energy-Arena, a dynamic benchmarking platform for operational energy time series forecasting that provides a continuously updated reference point as energy systems evolve. The platform operates as an open, API-based submission system and standardizes challenge definitions and submission deadlines aligned with operational constraints. Performance is reported on rolling evaluation windows via persistent leaderboards. By moving from retrospective backtesting to forward-looking benchmarking, the Energy-Arena enforces standardized ex-ante submission and ex-post evaluation, thereby improving transparency by preventing information leakage and retroactive tuning. The platform is publicly available at Energy-Arena.org. |
| title | Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting |
| topic | Econometrics Machine Learning |
| url | https://arxiv.org/abs/2604.24705 |