# Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service markets

arXiv: https://arxiv.org/abs/2601.14663 · HTML: https://mlsanity.com/paper/2601.14663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 51 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 19 of 19)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
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- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference](https://mlsanity.com/paper/1901.02731/) (2019)
- [Probabilistic Conformal Prediction Using Conditional Random Samples](https://mlsanity.com/paper/2206.06584/) (2022)
- [Is MC Dropout Bayesian?](https://mlsanity.com/paper/2110.04286/) (2021)
- [Copula Conformal Prediction for Multi-step Time Series Forecasting](https://mlsanity.com/paper/2212.03281/) (2022)
- [A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression](https://mlsanity.com/paper/2501.10533/) (2025)
- [Integrating Uncertainty Awareness into Conformalized Quantile Regression](https://mlsanity.com/paper/2306.08693/) (2023)
- [Conformalized Adaptive Forecasting of Heterogeneous Trajectories](https://mlsanity.com/paper/2402.09623/) (2024)
- [Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Markets](https://mlsanity.com/paper/2403.20149/) (2024)
- [Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach](https://mlsanity.com/paper/2501.06878/) (2025)
- [Joint Chance Constraints in AC Optimal Power Flow: Improving Bounds through Learning](https://mlsanity.com/paper/1809.04153/) (2018)
- [Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market](https://mlsanity.com/paper/2502.04935/) (2025)
- [Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey](https://mlsanity.com/paper/2101.08013/) (2021)
- [Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes](https://mlsanity.com/paper/2309.09012/) (2023)
- [Aggregator of Electric Vehicles Bidding in Nordic FCR-D Markets: A Chance-Constrained Program](https://mlsanity.com/paper/2404.12818/) (2024)

## Abstract (arXiv)

Reliable forecasting of prosumer flexibility is critical for demand response aggregators participating in frequency controlled ancillary services market, where strict reliability requirements such as the P90 standard are enforced. Limited historical data, dependence on exogeneous factors, and heterogenous prosumer behaviour introduce significant epistemic uncertainty, making deterministic or poorly calibrated probabilistic models unsuitable for market bidding. This paper proposes the use of scalable uncertainty quantification framework that integrates Monte Carlo dropout (MCD) with conformal prediction (CP) to produce calibrated, finite sample prediction intervals for aggregated prosumer flexibility. The proposed framework is applied to a behind-the-meter aggregator participating in the Danish manual frequency restoration reserve capacity market. A large-scale synthetic dataset is generated using a modified industry-grade home energy management system, combined with publicly available load, solar, price, activation and device-level data. The resulting machine learning surrogate model captures aggregate prosumer price responsiveness and provides uncertainty-aware estimates suitable for market bidding. Multiple multivariate CP strategies are evaluated and benchmarked against conventional MCD-based methods. Results show that standalone MCD systematically overestimates available flexibility and violates P90 compliance, whereas the proposed MCD-CP framework achieves reliable coverage with controlled conservatism. When embedded in aggregator bidding model, conformalised methods substantially reduce overbidding risk and achieve upto 70% of perfect-information profit while satisfying regulatory reliability constraints, providing practical, computationally efficient, and market-compliant solution for aggregator flexibility forecasting under uncertainty.