AutoPQ: Automating quantile estimation from point forecasts in the context of sustainability
Stefan Meisenbacher,
Kaleb Phipps,
Oskar Taubert,
Marie Weiel,
Markus Götz,
Ralf Mikut and
Veit Hagenmeyer
Applied Energy, 2025, vol. 392, issue C, No S0306261925006610
Abstract:
Optimizing smart grid operations relies on critical decision-making informed by uncertainty quantification, making probabilistic forecasting a vital tool. However, designing such forecasting models presents three key challenges: achieving accurate and unbiased uncertainty quantification, reducing the workload for data scientists during the design process, and minimizing the environmental impact of model training. In order to address these challenges, we introduce AutoPQ, a novel method that fully automates and optimizes probabilistic forecasting for smart grid applications. AutoPQ enhances forecast uncertainty quantification by generating high-quality quantile forecasts from an existing point forecast by using a conditional Invertible Neural Network (cINN). Furthermore, AutoPQ automates the selection of the optimal point forecasting method and fine-tunes hyperparameters, ensuring the best-possible model and configuration for each application. For flexible adaptation to various performance needs and available computing power, AutoPQ comes with a default and an advanced configuration, making it suitable for a wide range of smart grid applications. We demonstrate that AutoPQ surpasses state-of-the-art probabilistic forecasting methods while effectively limiting computational effort and hence environmental impact. Additionally and in the context of sustainability, AutoPQ provides full transparency regarding the electricity consumption required for performance improvements.
Keywords: Probabilistic time series forecasting; Uncertainty quantification; AutoML; Energy consumption (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925006610
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:392:y:2025:i:c:s0306261925006610
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.2025.125931
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 ().