Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters
نویسندگان
چکیده
Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up 30 MHz. A topology over 50 parameters calibrated using global sensitivity analysis transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed computes Sobol indices for each parameter applies TMCMC the most sensitive given topology. greedy random search refine discrete variables of network. This results in that can accurately compute transfer function despite noisy training data high dimensional space. able infer some produce data, under extrapolative scenarios.
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ژورنال
عنوان ژورنال: Energies
سال: 2021
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en14092402