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Development and Application of BIR-BEM: A Bayesian Inference R Platform for Building Energy Model Calibration.

Authors :
Danlin Hou
Hassan, Ibrahim Galal
Liangzhu (Leon) Wang
Source :
ASHRAE Transactions. 2022, Vol. 128 Issue Part2, p33-42. 10p.
Publication Year :
2022

Abstract

The building sector accounts for nearly 40% of global energy consumption and plays a critical role in societal energy security and sustainability. A building energy model (BEM) simulates complex building physics and provides insights into the performance of various energy-saving measures. The analysis based on BEMs has thus become an essential approach to slowing down the process of increasing building energy consumption. The reliability and accuracy of BEMs have a high impact on decision-making. However, how to calibrate a building energy model has remained a challenge. Existing calibrations are often deterministic without uncertainties quantified. In this study, a new automated multi-module calibration platform, BIRBEM (Bayesian Inference on R for Building Energy Model), is developed using an R programming language for calibrating building energy models. The sensitivity analysis module determines the calibration parameters, and the building energy model is replaced by the developed meta-model module for the Markov Chain Monte Carlo (MCMC) process to save computing time. An application of a high-rise residential building case in a hot and arid climate was demonstrated. The coefficient of variation with a root-mean-square error (CVRMSE) value of the monthly total cooling energy consumption is 13.95%, which satisfies the monthly calibration tolerance of 15% required by ASHRAE Guideline 14. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00012505
Volume :
128
Issue :
Part2
Database :
Academic Search Index
Journal :
ASHRAE Transactions
Publication Type :
Conference
Accession number :
164449783