Search

Abstract Sensitivity analysis (SA) can be applied to building energy models (BEM) to identify which input parameters that drive the majority of the model output variation. The screening-based Morris method is often applied for this purpose; however, consideration regarding the effect of the user-defined number of levels (p) and trajectories (r) on the obtained results are rare. This paper investigates how the choice of p and r affects the outcome of a SA using the Morris method on a high fidelity BEM. The results indicates that the Morris method was not able to replicate the ranking from the variance-based Sobol' method no matter the choice of r and p. It was, however, able to identify groups of input parameters (parameter clusters) most sensitive to the model output variability, but it required significantly more r than usually applied in studies featuring the Morris method. The reason is that marginal differences in absolute values of elementary effects (the sensitivity indices of the Morris method) for some input parameters may lead to a change in ranking position several times as the number of r increases. Users of the Morris method must therefore not be predetermined on the size of the parameter cluster; instead, they must make a visual assessment of the convergence of the parameter ranking to qualitatively determine the appropriate size of parameter cluster. The final recommendation for future studies deploying the Morris method for SA applied to a high fidelity BEM is to choose p ≥ 4 as it seems to lead the analysis towards a more truthful ranking, and then run simulations in steps of r = 100 when making the visual assessment to determine convergence and the size of parameter cluster. The identified need for more r questions the general notion that the Morris method is a computationally efficient screening method in terms of absolute time use. However, the Morris method is still much more computational efficient than a Sobol'-based analysis if the purpose of the SA is to identify a cluster of input parameters most sensitive to the model output variability. [ABSTRACT FROM AUTHOR]

Building life cycle assessment (LCA) aims at guiding decision towards more sustainable alternatives of a building project. But, in order to successfully reduce the high environmental impacts of the building sector, the results of building LCA have to be robust. Sensitivity analysis (SA) helps at investigating the sources of uncertainty and variability in a model and assessing the results' robustness. Choosing an adapted SA method remains difficult: using the more precise SA methods would require a too large calculation time, particularly for an application in early design phase. In this paper, the most influential factors of building LCA tools, linked with energy simulation tools, were investigated using screening and global SA methods that are compatible with the building LCA fields. The relative influence of the uncertain factors served as a metric to compare more precisely the results given by this large panel of methods. The results show that despite their different precision vs computational cost compromises, all the methods identified the same set of influential factors. In the studied case of a passive single family house, they consisted in the building lifetime, the electricity production mix and factors affecting the energy consumption. However, the relative influence of the factors differed. Two new method adaptations were investigated in order to obtain precise results in a reasonable simulation time. The adaptation of the Plackett and Burman (PB) screening performed well compared to the reference method (Sobol indices), as the same set of influential factors was always identified in a calculation time divided by almost 30. [ABSTRACT FROM AUTHOR]

Developing BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching. [ABSTRACT FROM AUTHOR]