The HEFTCom2024 winning model: A stacked CatBoost approach for probabilistic wind and solar power forecasting

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.