Multi-fidelity graph neural networks for efficient power flow analysis under high-dimensional demand and renewable generation uncertainty
The modernization of power systems faces uncertainties due to fluctuating renewable
energy sources, electric vehicle expansion, and demand response initiatives. These …
energy sources, electric vehicle expansion, and demand response initiatives. These …
Towards adoption of GNNs for power flow applications in distribution systems
An essential component of smart grid applications is the ability to solve the power flow (PF)
problem in real-time. As numerical methods are too slow, the use of neural networks (NNs) …
problem in real-time. As numerical methods are too slow, the use of neural networks (NNs) …
Uncertainty quantification analysis using multi-fidelity deep neural network
L Lv, J Zhao, J Chen - Journal of Physics: Conference Series, 2024 - iopscience.iop.org
In order to solve the highly-dimensional and highly-nonlinear problems in uncertainty
quantification of CFD, the paper develops a multi-fidelity neural network model and …
quantification of CFD, the paper develops a multi-fidelity neural network model and …