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PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

Author

Listed:
  • C. Ugwumadu

    (Quantum and Condensed Matter Physics (T-4) Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA)

  • J. Tabarez

    (Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA)

  • D. A. Drabold

    (Department of Physics and Astronomy, Ohio University, Athens, OH 45701, USA)

  • A. Pandey

    (Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA)

Abstract

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI , an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl , an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

Suggested Citation

  • C. Ugwumadu & J. Tabarez & D. A. Drabold & A. Pandey, 2025. "PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions," Energies, MDPI, vol. 18(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:1968-:d:1633032
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    References listed on IDEAS

    as
    1. Jianian Wang & Sheng Zhang & Yanghua Xiao & Rui Song, 2021. "A Review on Graph Neural Network Methods in Financial Applications," Papers 2111.15367, arXiv.org, revised Apr 2022.
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