On-line conformalized neural networks ensembles for probabilistic
  forecasting of day-ahead electricity prices
Alessandro Brusaferri Andrea Ballarino Luigi Grossi Fabrizio Laurini
Probabilistic electricity price forecasting (PEPF) is subject of increasing
interest, following the demand for proper quantification of prediction
uncertainty, to support the operation in complex power markets with increasing
share of renewable generation. Distributional neural networks ensembles have
been recently shown to outperform state of the art PEPF benchmarks. Still, they
require critical reliability enhancements, as fail to pass the coverage tests
at various steps on the prediction horizon. In this work, we propose a novel
approach to PEPF, extending the state of the art neural networks ensembles
based methods through conformal inference based techniques, deployed within an
on-line recalibration procedure. Experiments have been conducted on multiple
market regions, achieving day-ahead forecasts with improved hourly coverage and
stable probabilistic scores.