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

David S. Ching, Cosmin Safta and Thomas A. Reichardt
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David S. Ching: Sandia National Laboratories, 7011 East Ave., Livermore, CA 94550, USA
Cosmin Safta: Sandia National Laboratories, 7011 East Ave., Livermore, CA 94550, USA
Thomas A. Reichardt: Sandia National Laboratories, 7011 East Ave., Livermore, CA 94550, USA

Energies, 2021, vol. 14, issue 9, 1-21

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.

Keywords: power line communications (PLC); Bayesian inference; Transitional Markov Chain Monte Carlo; channel calibration; home network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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