A multilayer GNN fusing spatial, temporal, and co-occurrence edge types reports 30-day F1 of 0.8935 on substation maintenance prediction and eight separable risk clusters from Oklahoma Gas & Electric incident data.
Available on- line: https://energycentral.com/news/fact-sheetbiden-harris (accessed: 2025-03-20)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Multilayer GNN for Predictive Maintenance and Clustering in Power Grids
A multilayer GNN fusing spatial, temporal, and co-occurrence edge types reports 30-day F1 of 0.8935 on substation maintenance prediction and eight separable risk clusters from Oklahoma Gas & Electric incident data.