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Article

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

Sandia National Laboratories, 7011 East Ave., Livermore, CA 94550, USA
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Academic Editor: Alberto Sendin
Energies 2021, 14(9), 2402; https://doi.org/10.3390/en14092402
Received: 19 February 2021 / Revised: 16 April 2021 / Accepted: 19 April 2021 / Published: 23 April 2021
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. View Full-Text
Keywords: power line communications (PLC); Bayesian inference; Transitional Markov Chain Monte Carlo; channel calibration; home network power line communications (PLC); Bayesian inference; Transitional Markov Chain Monte Carlo; channel calibration; home network
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MDPI and ACS Style

Ching, D.S.; Safta, C.; Reichardt, T.A. Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters. Energies 2021, 14, 2402. https://doi.org/10.3390/en14092402

AMA Style

Ching DS, Safta C, Reichardt TA. Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters. Energies. 2021; 14(9):2402. https://doi.org/10.3390/en14092402

Chicago/Turabian Style

Ching, David S., Cosmin Safta, and Thomas A. Reichardt. 2021. "Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters" Energies 14, no. 9: 2402. https://doi.org/10.3390/en14092402

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