Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting

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.

Machine Learning (cs.LG)

G.3; I.2.1; I.2.6; J.2

arXiv:2606.09517 [cs.LG]

(or arXiv:2606.09517v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.09517 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1145/3765611.3815392 Focus to learn more DOI(s) linking to related resources

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