 The HEFTCom2024 winning model: A stacked CatBoost approach for probabilistic wind and solar power forecastingJon Olauson, Olle Viotti and Jakob Huss International Journal of Forecasting, 2026, vol. 42, issue 3, 724-735 Abstract: Effective energy trading requires probabilistic forecasts to quantify uncertainty and manage financial risk. In this paper, we describe our approach, which combines separate wind and solar models using a state-of-the-art stacked CatBoost framework. The effectiveness of this method was validated in the HEFTCom2024 competition, where it was the winning entry for forecasting and trading the combined generation of a 1200 MW offshore wind and 2400 MW solar portfolio in England. Key factors contributing to our success include the use of separate wind and solar power models, the incorporation of three different weather forecast datasets, no missed submissions (benchmark fills), and effective handling of a long-lasting cable issue for the offshore wind farm. Although the main focus was on the forecasting model, we also won the trading track. This is attributed mainly to our forecast accuracy, but our trading score exceeded expectations based on the trend in trading vs. the forecasting scores of co-competitors. Keywords: Probabilistic forecasting; Energy forecasting; Wind power; Solar power; Energy trading; Machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: 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:724-735 DOI: 10.1016/j.ijforecast.2026.02.005 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 |
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