Event Title

Evaluation of the impact of weather variability on a Net Zero Energy Building: advantage of sensitivity analysis for performance guarantee

Conference Editor

Jianshun Zhang; Edward Bogucz; Cliff Davidson; Elizabeth Krietmeyer

Location

Syracuse, NY

Event Website

http://ibpc2018.org/

Start Date

24-9-2018 10:30 AM

End Date

24-9-2018 12:00 PM

Description

Global sensitivity analysis associated with uncertainty analysis evaluates the robustness of a physical system and prioritises measurement and/or modelling efforts. The uncertainty analysis evaluates a confidence interval, whereas the sensitivity analysis quantifies the accountability of each uncertain input on the dispersion of the output. These statistical methods are usually used to account for the variability of the static inputs, which are constant regarding the evolution of the system, for example the physical properties of the materials modelled. Dynamic inputs however, i.e. parameters that are variable over time, are rarely taken into account in the statistical analyses because of the difficulty managing correlations between the inputs in stochastic methods. Yet, the system’s boundary conditions, such as meteorological input, are decisive for the evaluation of the behaviour of the building system. This paper aims at quantifying the influence of six meteorological variables as well as 39 static inputs on the dynamic thermal behaviour of a net zero energy building. To do so, a method that stochastically generates consistent meteorological data is used and is adapted to the purpose of global sensitivity analysis. The results show a high dispersion of the cooling requirements, for which the direct solar radiation, the albedo and the window solar factor can be held accountable. Thus the variability of solar resources and their interaction with the building have the greatest impact on the performance of the building. The variability of meteorological data needs to be considered to evaluate confidence intervals on energy performance. Furthermore, the impact of static parameters should not be overlooked, because their influence may remain significant. The considerable influence of the albedo and solar factor on the results of the present case study also shed light on the importance of assessing its value on site.

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Sep 24th, 10:30 AM Sep 24th, 12:00 PM

Evaluation of the impact of weather variability on a Net Zero Energy Building: advantage of sensitivity analysis for performance guarantee

Syracuse, NY

Global sensitivity analysis associated with uncertainty analysis evaluates the robustness of a physical system and prioritises measurement and/or modelling efforts. The uncertainty analysis evaluates a confidence interval, whereas the sensitivity analysis quantifies the accountability of each uncertain input on the dispersion of the output. These statistical methods are usually used to account for the variability of the static inputs, which are constant regarding the evolution of the system, for example the physical properties of the materials modelled. Dynamic inputs however, i.e. parameters that are variable over time, are rarely taken into account in the statistical analyses because of the difficulty managing correlations between the inputs in stochastic methods. Yet, the system’s boundary conditions, such as meteorological input, are decisive for the evaluation of the behaviour of the building system. This paper aims at quantifying the influence of six meteorological variables as well as 39 static inputs on the dynamic thermal behaviour of a net zero energy building. To do so, a method that stochastically generates consistent meteorological data is used and is adapted to the purpose of global sensitivity analysis. The results show a high dispersion of the cooling requirements, for which the direct solar radiation, the albedo and the window solar factor can be held accountable. Thus the variability of solar resources and their interaction with the building have the greatest impact on the performance of the building. The variability of meteorological data needs to be considered to evaluate confidence intervals on energy performance. Furthermore, the impact of static parameters should not be overlooked, because their influence may remain significant. The considerable influence of the albedo and solar factor on the results of the present case study also shed light on the importance of assessing its value on site.

https://surface.syr.edu/ibpc/2018/EP1/2