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The hybrid renewable energy forecasting and trading competition 2024

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  • Browell, Jethro
  • van der Meer, Dennis
  • Kälvegren, Henrik
  • Haglund, Sebastian
  • Simioni, Edoardo
  • Bessa, Ricardo J.
  • Wang, Yi

Abstract

The Hybrid Energy Forecasting and Trading Competition challenged participants to forecast and trade electricity generation from a 3.6 GW portfolio of wind and solar farms in Great Britain for three months in 2024. The competition mimicked operational practice, with participants required to submit genuine forecasts and market bids for the day ahead on a daily basis. Prizes were awarded for forecasting performance measured by the pinball score, trading performance measured by total revenue, and combined performance based on rank in the other two tracks. Here, we present an analysis of the participants’ performance and the lessons learned from the competition. The forecasting track reaffirmed the competitiveness of popular gradient boosted tree algorithms for day-ahead wind and solar power forecasting, though other methods also yielded strong results, with performance in all cases highly dependent on implementation. The trading track offers insight into the relationship between forecast skill and value, with trading strategy and underlying forecasts influencing performance. All competition data, including power production, weather forecasts, electricity market data, and participants’ submissions, are shared for further analysis and benchmarking.

Suggested Citation

  • Browell, Jethro & van der Meer, Dennis & Kälvegren, Henrik & Haglund, Sebastian & Simioni, Edoardo & Bessa, Ricardo J. & Wang, Yi, 2026. "The hybrid renewable energy forecasting and trading competition 2024," International Journal of Forecasting, Elsevier, vol. 42(3), pages 709-723.
    • Handle: RePEc:eee:intfor:v:42:y:2026:i:3:p:709-723
    DOI: 10.1016/j.ijforecast.2025.10.005
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    References listed on IDEAS

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