Int. J. Metrol. Qual. Eng.
Volume 6, Number 3, 2015
|Number of page(s)
|23 October 2015
Uncertainty assessment in building energy performance with a simplified model
1 L’UNAM, LARIS, Systems Engineering
Research Laboratory, University of Angers, 62 avenue Notre Dame du Lac, Angers,
2 Cerema, risk, environment, mobility and development research Center, 23 avenue de l’Amiral Chauvin, 49136 Les Ponts-de-Cé, France
Accepted: 7 September 2015
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.
Key words: Building energy performance / Uncertainty evaluation / GUM Method / Bayesian Approach / Monte Carlo
© EDP Sciences 2015
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