Autor: |
David S. Ching, Cosmin Safta, Thomas A. Reichardt |
Jazyk: |
angličtina |
Rok vydání: |
2021 |
Předmět: |
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Zdroj: |
Energies, Vol 14, Iss 9, p 2402 (2021) |
Druh dokumentu: |
article |
ISSN: |
1996-1073 |
DOI: |
10.3390/en14092402 |
Popis: |
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. |
Databáze: |
Directory of Open Access Journals |
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