A stacked denoising autoencoder learns the mapping from operating conditions to OPF solutions so that Monte-Carlo samples for probabilistic OPF can be evaluated without repeated nonlinear optimization.
Probabilistic power flow by Monte Carlo simulation with latin supercube sampling
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Fast Calculation of Probabilistic Optimal Power Flow: A Deep Learning Approach
A stacked denoising autoencoder learns the mapping from operating conditions to OPF solutions so that Monte-Carlo samples for probabilistic OPF can be evaluated without repeated nonlinear optimization.