Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, Cornell Tech, Cornell University, New York City, NY, USA
2. Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Funder
National Science Foundation
Director, Cybersecurity, Energy Security, and Emergency Response, Cybersecurity for Energy Delivery Systems Program, of the U.S. Department of Energy
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Computer Science
Link
https://ieeexplore.ieee.org/ielam/5165411/10226523/10025850-aam.pdf
Reference43 articles.
1. State Estimation in Electric Power Systems Leveraging Graph Neural Networks
2. Graph Neural Networks: Architectures, Stability, and Transferability
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4. Geometric Deep Learning: Going beyond Euclidean data
5. Forecasting-Aided State Estimation—Part II: Implementation
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