Core Concepts
A novel approach to probabilistic electricity price forecasting that extends state-of-the-art neural network ensembles through conformal inference techniques, deployed within an on-line recalibration procedure, to achieve improved hourly coverage and stable probabilistic scores.
Abstract
The content discusses the problem of probabilistic electricity price forecasting (PEPF), which is of increasing interest due to the demand for proper quantification of prediction uncertainty to support operations in complex power markets with increasing renewable generation.
The key highlights and insights are:
Distributional neural network ensembles have been shown to outperform state-of-the-art PEPF benchmarks, but they lack the required calibration capabilities, failing to pass coverage tests at various prediction horizons.
The authors propose a novel approach to PEPF that extends the deep ensemble-based methods through conformal inference techniques, deployed within an on-line recalibration procedure.
The developed method leverages asymmetric conformal prediction for regression tasks, enabling flexible step-wise compensations of the upper/lower prediction bands beyond the marginal coverage.
It also integrates adversarial conformal inference to address the lack of robustness of conformal prediction under non-exchangeable conditions, such as distribution shifts.
The authors explore both quantile regression and distributional neural networks to estimate the conditional quantiles to be calibrated, and employ a uniform vincentization technique for ensemble aggregation.
Experiments are conducted on multiple market regions, including the German and Italian day-ahead markets, achieving improved hourly coverage and stable probabilistic scores compared to state-of-the-art benchmarks.
Stats
The content does not provide specific numerical data or metrics, but rather focuses on the methodological development and experimental evaluation of the proposed probabilistic electricity price forecasting approach.
Quotes
The content does not contain any striking quotes that support the key logics.