The hybrid renewable energy forecasting and trading competition 2024

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

International Journal of Forecasting, 2026, vol. 42, issue 3, 709-723

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.

Keywords: Energy forecasting; Energy trading; Forecasting competition; Wind power; Solar power; Probabilistic forecasting (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207025001013
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:42:y:2026:i:3:p:709-723

DOI: 10.1016/j.ijforecast.2025.10.005

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().