Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting
Jan Niklas Lettner, Hadeer El Ashhab, Benjamin Schäfer
Abstract
As renewable energy integration increases market volatility, probabilistic electricity price forecasting has become essential for effective risk management. However, current-proper-scoring rules often prioritize forecast sharpness at the expense of calibration, leading to overconfident and statistically unreliable uncertainty estimates. This work highlights the critical gap between theoretical scoring and practical calibration, demonstrating that models can become mere proxies for deterministic forecasts when reliability is neglected. We conclude that future research must shift toward calibration-aware objectives and architectures to ensure the distributional integrity of energy market forecasts.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.