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Wu, L., Kong, C., Hao, X. and Chen, W. (2020) A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model. Mathematical Problems in Engineering, 2020, Article ID: 1428104.
https://doi.org/10.1155/2020/1428104

has been cited by the following article:

• TITLE: Advanced Quantum Support Vector Machine Algorithm for Transient Stability Assessment

  AUTHORS: Junior Morel Angouah Massaga, Claudette Christiane Koupna Eko, Patrick Nounamo Dabou, Jacques Tagoudjeu

  KEYWORDS: Power System, Quantum Machine Learning, IEEE 39-Bus, IEEE 68-Bus, Quantum Programming, Transient Stability, Assessment

  JOURNAL NAME: Advances in Artificial Intelligence and Robotics Research, Vol.2 No.3, September 9, 2026

  ABSTRACT: The increasing use of smart sensors in electrical grids is generating a massive amount of data, creating new challenges for power systems professionals. To effectively analyze these large datasets for signs of instability, quantum algorithms are a promising solution. This work demonstrates that Advanced Quantum Support Vector Machines (AQSVMs) are a superior alternative to classical SVMs for the rapid and accurate assessment of dynamic stability from this data. We implement a quantum circuit to encode data, train the model, and measure qubits, enabling the AQSVM to evaluate the probability of stability for any given signal. Tested on massive labeled datasets generated from IEEE 39-bus and 68-bus systems, the AQSVM consistently achieved exceptional performance (e.g., 99.13% precision, 99.37% accuracy, 99.99% AUC, and 1.38% Log-loss for the 68-bus case), outperforming its classical counterpart.