Uncertainty assessment in building energy performance with a simplified model
To assess a building energy performance, the consumption being predicted or estimated during the design stage is compared to the measured consumption when the building is operational. When valuing this performance, many buildings show significant differences between the...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
EDP Sciences
2015-01-01
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Series: | International Journal of Metrology and Quality Engineering |
Subjects: | |
Online Access: | https://doi.org/10.1051/ijmqe/2015022 |
Summary: | To assess a building energy performance, the consumption being predicted or estimated
during the design stage is compared to the measured consumption when the building is
operational. When valuing this performance, many buildings show significant differences
between the calculated and measured consumption. In order to assess the performance
accurately and ensure the thermal efficiency of the building, it is necessary to evaluate
the uncertainties involved not only in measurement but also those induced by the
propagation of the dynamic and the static input data in the model being used. The
evaluation of measurement uncertainty is based on both the knowledge about the measurement
process and the input quantities which influence the result of measurement. Measurement
uncertainty can be evaluated within the framework of conventional statistics presented in
the Guide to the Expression of Measurement Uncertainty (GUM) as well as by Bayesian
Statistical Theory (BST). Another choice is the use of numerical methods like Monte Carlo
Simulation (MCS). In this paper, we proposed to evaluate the uncertainty associated to the
use of a simplified model for the estimation of the energy consumption of a given
building. A detailed review and discussion of these three approaches (GUM, MCS and BST) is
given. Therefore, an office building has been monitored and multiple temperature sensors
have been mounted on candidate locations to get required data. The monitored zone is
composed of six offices and has an overall surface of 102 m2. |
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ISSN: | 2107-6839 2107-6847 |