The Hybrid Renewable Energy Forecasting and Trading Competition 2024

TL;DR

The 2024 Hybrid Renewable Energy Forecasting and Trading Competition evaluated forecasting and trading strategies for wind and solar power in Great Britain, highlighting algorithm effectiveness, forecast-trade relationships, and sharing comprehensive data for future research.

Contribution

This paper introduces a large-scale competition framework for renewable energy forecasting and trading, providing insights into algorithm performance and the impact of forecast accuracy on trading success.

Findings

Gradient boosted trees perform well in wind and solar forecasting.

Forecast accuracy significantly influences trading profitability.

Sharing comprehensive data enables benchmarking and further research.

Abstract

The Hybrid Energy Forecasting and Trading Competition challenged participants to forecast and trade the electricity generation from a 3.6GW 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 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 learnings from the competition. The forecasting track reaffirms 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.