Abstract
In this study, we examine the efficiency and reliability of a Bayesian calibration setup using temperature point measurements. Hamiltonian Monte Carlo sampling is found to be significantly more efficient with regard to convergence of the posterior distributions, which is assessed using different visual and quantitative measures. The examination of posterior realizations from different data sets and different prior distributions reveals that inference about model parameters is in general quite reliable, while learning about the magnitude of different error terms, such as model discrepancy and random errors, proves to be more difficult. Finally, predictive simulation results based on these inferred posterior distributions are generally in good agreement with measured data.
Original language | English |
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Title of host publication | 15th International Conference of the International Building Performance Simulation Association, Building Simulation 2017 |
Editors | Charles S. Barnaby, Michael Wetter |
Publisher | International Building Performance Simulation Association |
Pages | 572-581 |
Number of pages | 10 |
ISBN (Electronic) | 9781510870673 |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | 15th International Conference of the International Building Performance Simulation Association, Building Simulation 2017 - San Francisco, United States Duration: 2017 Aug 7 → 2017 Aug 9 |
Publication series
Name | Building Simulation Conference Proceedings |
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Volume | 2 |
ISSN (Print) | 2522-2708 |
Conference
Conference | 15th International Conference of the International Building Performance Simulation Association, Building Simulation 2017 |
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Country/Territory | United States |
City | San Francisco |
Period | 17/8/7 → 17/8/9 |
Bibliographical note
Funding Information:This study is supported by EPSRC grant (EP/L024452/1): Bayesian Building Energy Management (B-bem).
Publisher Copyright:
© 2017 Building Simulation Conference Proceedings. All rights reserved.
ASJC Scopus subject areas
- Building and Construction
- Architecture
- Modelling and Simulation
- Computer Science Applications