TY - GEN
T1 - Comparative Analysis of Spatial and Spectral Methods in GNN for Power Flow in Electrical Power Systems
AU - Espinoza, Paulo A.
AU - Ruz, Gonzalo A.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - This paper explores the application of Graph Neural Networks (GNNs) to power flow problems, comparing several spectral and spatial methods. The research reveals that spatial methods generally outperform their spectral counterparts, which do not rely on spectral theory, eigenvalues, or eigenvectors. GraphSAGE [9] demonstrates the best performance among the spatial methods tested, achieving a Mean Absolute Percentage Error (MAPE) of 0.79% on the test set in an experiment with 14-buses and 0.53% in the experiment with 30-buses. These findings suggest that for power flow problems, it is beneficial to consider at least hybrid or predominantly spatial models that leverage information from non-immediate neighbors. This research highlights the potential of spatial GNN methods in accurately capturing the complexities of power systems, paving the way for more robust and efficient solutions in the domain.
AB - This paper explores the application of Graph Neural Networks (GNNs) to power flow problems, comparing several spectral and spatial methods. The research reveals that spatial methods generally outperform their spectral counterparts, which do not rely on spectral theory, eigenvalues, or eigenvectors. GraphSAGE [9] demonstrates the best performance among the spatial methods tested, achieving a Mean Absolute Percentage Error (MAPE) of 0.79% on the test set in an experiment with 14-buses and 0.53% in the experiment with 30-buses. These findings suggest that for power flow problems, it is beneficial to consider at least hybrid or predominantly spatial models that leverage information from non-immediate neighbors. This research highlights the potential of spatial GNN methods in accurately capturing the complexities of power systems, paving the way for more robust and efficient solutions in the domain.
KW - Graph Convolutional Networks
KW - Graph Neural Networks
KW - Power Flow
KW - Power Transmission Systems
UR - http://www.scopus.com/inward/record.url?scp=85210236499&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-76604-6_2
DO - 10.1007/978-3-031-76604-6_2
M3 - Conference contribution
AN - SCOPUS:85210236499
SN - 9783031766039
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 16
EP - 29
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 27th Iberoamerican Congress, CIARP 2024, Proceedings
A2 - Hernández-García, Ruber
A2 - Barrientos, Ricardo J.
A2 - Velastin, Sergio A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2024
Y2 - 26 November 2024 through 29 November 2024
ER -