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Research PaperResearchia:202604.28072

Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting

Max Kleinebrahm

Abstract

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 ti...

Submitted: April 28, 2026Subjects: Machine Learning; Data Science

Description / Details

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.


Source: arXiv:2604.24705v1 - http://arxiv.org/abs/2604.24705v1 PDF: https://arxiv.org/pdf/2604.24705v1 Original Link: http://arxiv.org/abs/2604.24705v1

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Date:
Apr 28, 2026
Topic:
Data Science
Area:
Machine Learning
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