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Meisenbacher, S.* ; Phipps, K.* ; Taubert, O.* ; Weiel, M.* ; Götz, M.* ; Mikut, R.* ; Hagenmeyer, V.*

AutoPQ: Automating quantile estimation from point forecasts in the context of sustainability.

Appl. Energy 392, 24 (2025)
Verlagsversion DOI
Open Access Hybrid
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Automl ; Energy Consumption ; Probabilistic Time Series Forecasting ; Uncertainty Quantification; Optimal Power-flow; Time-series; Probabilistic-forecasts; Prediction; Regression; Generation; Forest; Model; Risk
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 0306-2619
Zeitschrift Applied Energy
Quellenangaben Band: 392, Heft: , Seiten: 24 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz AI - KIT (HAI - KIT)
Förderungen Federal Ministry of Education and Research
Ministry of Science, Research and the Arts Baden-Wurttemberg
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
Helmholtz Association under the Program "Energy System Design"
DOI 10.1016/j.apenergy.2025.125931
WOS ID 001479846500001
Scopus ID 105003243447
Erfassungsdatum 2025-04-30
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