Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting

Energy forecasting plays a crucial role in modern power systems, especially with the increasing penetration of renewable energy and market volatility. In recent years, research in the field of energy forecasting has shown significant growth, particularly in electricity price forecasting, load forecasting, and renewable energy generation forecasting. Despite this, the field lacks a widely accepted, continuously updated dynamic benchmark that captures the evolving conditions of modern energy systems. This makes it difficult to assess state-of-the-art forecasting performance, hindering consistent and measurable progress in both research and commercial practice.

The core problem in energy forecasting research is the lack of a dynamic, continuously updated benchmark to evaluate model performance. Most research relies on study-specific benchmark datasets, evaluation metrics, and experimental designs, which limit model comparisons to within the same study and make it difficult to compare across different studies. Even when addressing the same target variable, such as day-ahead electricity prices, models are evaluated on different time periods, market zones, preprocessing pipelines, and information sets. This results in reported performance differences often reflecting experimental design choices rather than methodological advances.

The core innovation of the Energy-Arena platform lies in its dynamic, API-driven architecture that enables automated forward evaluation and provides continuously updated leaderboards. • Forecasting challenges are defined through configuration files specifying the framework conditions, such as the target variable and temporal structure. • Participants interact with the platform through a web-based frontend, submitting forecasts and viewing leaderboards. • The backend system validates forecasts, enforces submission deadlines, and stores participants, submissions, ground-truth values, and scoring results. • The platform also supports reference benchmark models to provide performance baselines.

The architecture of the Energy-Arena platform includes a modular, API-driven benchmarking system that integrates standardized challenge definitions, participant interaction, automated scoring, and continuously updated leaderboards. • Forecasting challenges are defined through configuration files, and participants interact with the platform through a web-based frontend, submitting forecasts and viewing leaderboards. • The backend system validates forecasts, enforces submission deadlines, and stores participants, submissions, ground-truth values, and scoring results. • The platform also supports reference benchmark models to provide performance baselines. • Ground-truth data are retrieved from external data providers (e.g., ENTSO-E) and processed through a worker pipeline that periodically ingests new observations, evaluates pending submissions, and updates leaderboard aggregates.

The experimental design of the Energy-Arena platform includes using ground-truth data from external data providers like ENTSO-E, processed through a worker pipeline that periodically ingests new observations, evaluates pending submissions, and updates leaderboard aggregates. The platform supports reference benchmark models to provide performance baselines. These models may include simple statistical benchmarks (e.g., persistence or seasonal naïve forecasts) as well as more advanced machine learning models. Participants can choose whether their submitted forecast trajectories are publicly visualized or whether only aggregated performance metrics are displayed.

The Energy-Arena platform provides transparent model comparison through continuously updated leaderboards, significantly enhancing transparency and comparability in energy forecasting. By enforcing standardized forward-looking benchmarking, the platform prevents information leakage and retroactive tuning, enhancing research transparency. The platform's openness and API-driven submission system enable researchers and commercial users to benchmark performance under real-world market dynamics.

The application scenarios of the Energy-Arena platform include: • Researchers can continuously compare models under common evaluation conditions, facilitating cumulative scientific progress. • Practitioners and commercial providers can benchmark forecasting performance under real-world market dynamics. • The platform can also support educational applications, such as university forecasting challenges, where students develop and deploy forecasting models on the platform.

The limitations of the Energy-Arena platform include: • The platform relies on the data quality and timeliness of external data providers, which may affect forecast accuracy and leaderboard updates. • Since the platform does not restrict the use of exogenous variables, comparisons across different information sets may lack transparency. • Participants need a certain level of technical skill to use the API for forecast submissions.