On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices

Alessandro Brusaferri, Andrea Ballarino, Luigi Grossi and Fabrizio Laurini

Applied Energy, 2025, vol. 398, issue C, No S0306261925011420

Abstract: 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 network ensembles have been recently shown to outperform state of the art PEPF benchmarks. Still, they require critical reliability enhancements, as they 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 methods based on neural network ensembles through conformal-inference 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.

Keywords: Conformal prediction; Day-ahead market; Electricity price; Ensembles; Neural networks; Probabilistic forecasting; Time series (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925011420
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:398:y:2025:i:c:s0306261925011420

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.126412

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().