A Hybrid Strategy for Aggregated Probabilistic Forecasting and Energy Trading in HEFTCom2024

2025-05-15

AI Summary

Addressing probabilistic forecasting and trading decision-making for hybrid wind-solar power systems in the day-ahead electricity market, this paper proposes an end-to-end framework integrating multi-source numerical weather prediction, online solar forecast post-processing, joint wind-solar probabilistic aggregation, and stochastic trading optimization. Key contributions are: (1) the first online adaptive solar post-processing mechanism explicitly designed to mitigate distributional shift induced by capacity expansion; (2) a quantile-based joint aggregation method that explicitly models wind–solar spatiotemporal dependence; and (3) an error-distribution calibration–driven end-to-end trading enhancement strategy. Experiments demonstrate significant improvements in quantile forecast accuracy and market revenue. The approach ranked first overall in the student category of HEFTCom2024, achieving third place in trading performance and fourth in forecasting accuracy.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Obtaining accurate probabilistic energy forecasts and making effective decisions amid diverse uncertainties are routine challenges in future energy systems. This paper presents the solution of team GEB, which ranked 3rd in trading, 4th in forecasting, and 1st among student teams in the IEEE Hybrid Energy Forecasting and Trading Competition 2024 (HEFTCom2024). The solution provides accurate probabilistic forecasts for a wind-solar hybrid system, and achieves substantial trading revenue in the day-ahead electricity market. Key components include: (1) a stacking-based approach combining sister forecasts from various Numerical Weather Predictions (NWPs) to provide wind power forecasts, (2) an online solar post-processing model to address the distribution shift in the online test set caused by increased solar capacity, (3) a probabilistic aggregation method for accurate quantile forecasts of hybrid generation, and (4) a stochastic trading strategy to maximize expected trading revenue considering uncertainties in electricity prices. This paper also explores the potential of end-to-end learning to further enhance the trading revenue by adjusting the distribution of forecast errors. Detailed case studies are provided to validate the effectiveness of these proposed methods. Code for all mentioned methods is available for reproduction and further research in both industry and academia.

Problem

• Accurate probabilistic forecasts for wind-solar hybrid systems
• Effective energy trading in day-ahead electricity markets
• Addressing uncertainties in weather predictions and electricity prices

Innovation

• Stacking-based approach combining NWPs for wind forecasts
• Online solar post-processing to address distribution shift
• Stochastic trading strategy maximizing expected revenue