Calibrated uncertainty quantification for prosumer flexibility 2027 Applied

Reliable forecasting of prosumer flexibility is critical for demand response aggregators participating in frequency controlled ancillary service markets, where reliability requirements such as the P90 standard are enforced. Limited behavioural data and dependence on exogenous forecasts introduce uncertainty into flexibility estimates, which can lead to reserve overestimation and financial penalties. This study applies and systematically evaluates established uncertainty quantification approaches based on Monte Carlo dropout (MCD), conformal prediction (CP), and conformalised quantile regression (CQR) to construct prediction intervals for aggregated prosumer flexibility and support uncertainty-aware aggregator bidding. The approaches are evaluated for a behind-the-meter aggregator participating in the Danish manual frequency restoration reserve capacity market. A large-scale synthetic dataset is generated using an industry-grade home energy management system, combined with publicly available load, solar, price, activation, and device-level data. Multiple multivariate CP strategies are evaluated using MCD predictions, alongside CQR using a separately trained quantile regression model. Under nominal synthetic test conditions, conformal methods substantially improve joint coverage compared with uncalibrated MCD baselines. More conservative methods satisfy the 90% lower-tail reliability requirement, whi.