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A General Framework for Uncertainty Quantification via Neural SDE-RNN
Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially for the time series imputation with irregularly sampled measurements.
Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids
Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness.
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems
Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System
Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness.
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