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DOE OSTI · 1779573

Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters

Ching, David S. (ORCID:0000000229621252)·Safta, Cosmin (ORCID:0000000172197736)·Reichardt, Thomas A. (ORCID:0000000227716653)
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Abstract

Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.

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42 ENGINEERING97 MATHEMATICS AND COMPUTINGBayesian inferenceTransitional Markov Chain Monte Carlochannel calibrationhome networkpower line communications (PLC)

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BibTeXRIS

Ching, David S. (ORCID:0000000229621252), Safta, Cosmin (ORCID:0000000172197736), Reichardt, Thomas A. (ORCID:0000000227716653). 2021-04-23. Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters. https://doi.org/10.3390/en14092402

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