# Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting

arXiv: https://arxiv.org/abs/2606.09517 · HTML: https://mlsanity.com/paper/2606.09517/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 2 of 2)

- [Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification](https://mlsanity.com/paper/2011.09588/) (2020)
- [Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows](https://mlsanity.com/paper/2206.06672/) (2022)

## Abstract (arXiv)

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.