The hybrid renewable energy forecasting and trading competition 2024

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.

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Publication Details

Journal
International Journal of Forecasting
Published
2025-11-01
DOI
https://doi.org/10.1016/j.ijforecast.2025.10.005
Citations
1
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.73
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The hybrid renewable energy forecasting and trading competition 2024

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The hybrid renewable energy forecasting and trading competition 2024

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article en
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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.

International Journal of Forecasting
Statistics Denmark (DK), ReVibe Energy (Sweden) (SE), INESC TEC (PT), University of Glasgow (GB), University of Hong Kong (HK)
Openalex Percentile: Top 21%
Energy Load and Power Forecasting
0.73
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