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Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration

Many factors contribute to the inherent uncertainty of energy consumption modeling in buildings. It is essential to perform a calibration and sensitivity analysis in order to manage these uncertainties. Despite the availability of several calibration methods, they are often deterministic and lack qu...

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Autores principales: Hou, Danlin, Zhan, Dongxue, Wang, Liangzhu, Hassan, Ibrahim Galal, Sezer, Nurettin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627152/
https://www.ncbi.nlm.nih.gov/pubmed/37936825
http://dx.doi.org/10.1007/s44245-023-00027-2
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author Hou, Danlin
Zhan, Dongxue
Wang, Liangzhu
Hassan, Ibrahim Galal
Sezer, Nurettin
author_facet Hou, Danlin
Zhan, Dongxue
Wang, Liangzhu
Hassan, Ibrahim Galal
Sezer, Nurettin
author_sort Hou, Danlin
collection PubMed
description Many factors contribute to the inherent uncertainty of energy consumption modeling in buildings. It is essential to perform a calibration and sensitivity analysis in order to manage these uncertainties. Despite the availability of several calibration methods, they are often deterministic and lack quantified uncertainties. Moreover, the selection of parameters in building energy modeling for calibration depends on the user’s experience. Therefore, a more rigorous selection process is required. This study developed a new automated Bayesian Inference calibration platform running as an R package. A sensitivity analysis module and a Bayesian inference module determine the calibration parameters and uncertainties, respectively. The Meta-model module is developed to replace the building energy model for the Markov Chain Monte Carlo process to save computing time. The proposed platform is successfully demonstrated on a synthetic high-rise office building and a real high-rise residential building in a hot and arid climate. The relationship between the number of calibration parameters, calibration performance, and the accuracy of the Meta-model is further discussed. The developed calibration platform in this study proved to have clear advantages over the existing platforms, with the ability to reasonably estimate building energy performance in a short computing time.
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spelling pubmed-106271522023-11-07 Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration Hou, Danlin Zhan, Dongxue Wang, Liangzhu Hassan, Ibrahim Galal Sezer, Nurettin Discov Mech Eng Research Many factors contribute to the inherent uncertainty of energy consumption modeling in buildings. It is essential to perform a calibration and sensitivity analysis in order to manage these uncertainties. Despite the availability of several calibration methods, they are often deterministic and lack quantified uncertainties. Moreover, the selection of parameters in building energy modeling for calibration depends on the user’s experience. Therefore, a more rigorous selection process is required. This study developed a new automated Bayesian Inference calibration platform running as an R package. A sensitivity analysis module and a Bayesian inference module determine the calibration parameters and uncertainties, respectively. The Meta-model module is developed to replace the building energy model for the Markov Chain Monte Carlo process to save computing time. The proposed platform is successfully demonstrated on a synthetic high-rise office building and a real high-rise residential building in a hot and arid climate. The relationship between the number of calibration parameters, calibration performance, and the accuracy of the Meta-model is further discussed. The developed calibration platform in this study proved to have clear advantages over the existing platforms, with the ability to reasonably estimate building energy performance in a short computing time. Springer International Publishing 2023-10-31 2023 /pmc/articles/PMC10627152/ /pubmed/37936825 http://dx.doi.org/10.1007/s44245-023-00027-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Hou, Danlin
Zhan, Dongxue
Wang, Liangzhu
Hassan, Ibrahim Galal
Sezer, Nurettin
Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration
title Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration
title_full Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration
title_fullStr Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration
title_full_unstemmed Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration
title_short Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration
title_sort development and performance assessment of a new opensource bayesian inference r platform for building energy model calibration
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627152/
https://www.ncbi.nlm.nih.gov/pubmed/37936825
http://dx.doi.org/10.1007/s44245-023-00027-2
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