Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/9/2402 |
| _version_ | 1797536563837534208 |
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| author | David S. Ching Cosmin Safta Thomas A. Reichardt |
| author_facet | David S. Ching Cosmin Safta Thomas A. Reichardt |
| author_sort | David S. Ching |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:02:30Z |
| format | Article |
| id | doaj.art-176a116be7114ad5b796b786e9f5f340 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T12:02:30Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-176a116be7114ad5b796b786e9f5f3402023-11-21T16:49:47ZengMDPI AGEnergies1996-10732021-04-01149240210.3390/en14092402Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown ParametersDavid S. Ching0Cosmin Safta1Thomas A. Reichardt2Sandia National Laboratories, 7011 East Ave., Livermore, CA 94550, USASandia National Laboratories, 7011 East Ave., Livermore, CA 94550, USASandia National Laboratories, 7011 East Ave., Livermore, CA 94550, USABayesian 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.https://www.mdpi.com/1996-1073/14/9/2402power line communications (PLC)Bayesian inferenceTransitional Markov Chain Monte Carlochannel calibrationhome network |
| spellingShingle | David S. Ching Cosmin Safta Thomas A. Reichardt Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters Energies power line communications (PLC) Bayesian inference Transitional Markov Chain Monte Carlo channel calibration home network |
| title | Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters |
| title_full | Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters |
| title_fullStr | Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters |
| title_full_unstemmed | Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters |
| title_short | Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters |
| title_sort | sensitivity informed bayesian inference for home plc network models with unknown parameters |
| topic | power line communications (PLC) Bayesian inference Transitional Markov Chain Monte Carlo channel calibration home network |
| url | https://www.mdpi.com/1996-1073/14/9/2402 |
| work_keys_str_mv | AT davidsching sensitivityinformedbayesianinferenceforhomeplcnetworkmodelswithunknownparameters AT cosminsafta sensitivityinformedbayesianinferenceforhomeplcnetworkmodelswithunknownparameters AT thomasareichardt sensitivityinformedbayesianinferenceforhomeplcnetworkmodelswithunknownparameters |