If you want to make money in energy, a 'more accurate' forecast is actually a waste of time.
April 16, 2026
Original Paper
When Forecast Accuracy Fails: Rank Correlation and Decision Quality in Multi-Market Battery Storage Optimization
arXiv · 2604.12082
The Takeaway
In battery storage trading, everyone obsesses over Mean Absolute Error (how close your price prediction is to reality). But this study shows that standard accuracy is a poor predictor of actual revenue. What actually matters is 'rank correlation'—simply knowing if the price will be higher in hour A than hour B, even if your specific numbers are way off. It turns out that being 'vaguely right' about the order of things is much more profitable than being 'precisely wrong' about the exact price. This challenges the entire multi-billion dollar industry built on refining predictive math. For the energy grid, it means we’ve been optimizing for the wrong mathematical goal for years.
From the abstract
Battery energy storage systems (BESS) participating in multi-market electricity trading require price forecasts to optimize dispatch decisions. A widely held assumption is that forecast accuracy, measured by standard metrics such as mean absolute error (MAE), drives trading performance. We challenge this assumption using a hierarchical three-layer optimization system trading simultaneously on frequency containment reserve (FCR), automatic frequency restoration reserve (aFRR), day-ahead, and cont