Application of sensitivity analysis in building energy simulations: Combining first- and second-order elementary effects methods

• Published in: Energy and buildings 2014-01, Vol.68, p.741-750
• Main Authors: Garcia Sanchez, D., Lacarrière, B., Musy, M., Bourges, B.
• Format: Article
• Language: English
• Subjects: Applied sciences; Building technical equipments; Building thermal simulation; Buildings; Buildings. Public works; Computation methods. Tables. Charts; Computer simulation; Elementary effects; Energy demand; Energy management and energy conservation in building; Energy use; Engineering Sciences; Environmental engineering; Exact sciences and technology; Intervals; Management; Mathematical models; Morris method; Nonlinearity; Sensitivity analysis; Structural analysis. Stresses
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2012.08.048

► We combine 1st and 2nd order of sensitivity analysis applied to ESP-r through a case study. ► We implement Morris method and its extension to second order. ► We illustrate the potential offered by various outputs to improve the analyze. ► We propose solutions to differentiate non-linearity from higher order interaction. Sensitivity analysis plays an important role in the understanding of complex models. It helps to identify the influence of input parameters in relation to the outputs. It can also be a tool to understand the behavior of the model and can then facilitate its development stage. This study aims to analyze and illustrate the potential usefulness of combining first and second-order sensitivity analysis, applied to a building energy model (ESP-r). Through the example of an apartment building, a sensitivity analysis is performed using the method of elementary effects (also known as the Morris method), including an analysis of the interactions between the input parameters (second-order analysis). The usefulness of higher-order analysis is highlighted to support the results of the first-order analysis better. Several aspects are tackled to implement the multi-order sensitivity analysis efficiently: interval size of the variables, the management of non-linearity and the usefulness of various outputs.