Evaluation of capabilities of different global sensitivity analysis techniques for building energy simulation: experiment on design variables

Abstract

The objective of this study is to investigate the capabilities of different global sensitivity analysis methods applied to building performance simulation, i.e. Morris, Monte Carlo, Design of Experiments, and Sobol methods. A single-zone commercial building located in Florianópolis, southern Brazil, was used as a case study. Fifteen inputs related to design variables were considered, such as thermal properties of the construction envelope, solar orientation, and fenestration characteristics. The performance measures were the annual heating and cooling loads. It was found that each method can provide different visual capabilities and measures of interpretation, but, in general, there was little difference in showing the most influent and least influent variables. For the heating loads, the thermal transmittances were the most influent variables, while for the cooling loads, the solar absorptances stood out. The Morris method showed to be the most feasible method due to its simplicity and low computational cost. However, as the building simulation model is still complex and non-linear, the variance-based method such as the Sobol is still necessary for general purposes.

Analysis of Hepatic Lipid Metabolism Model: Simulation and Non-Stationary Global Sensitivity Analysis

“…Alternatively, global sensitivity analysis considers simulated changes of all parameter sets over a specified range [ 16 , 17 ]. Over the past years, different global sensitivity analysis methods such as the Moriss method [ 18 , 19 , 20 ], Sobol’s method [ 21 , 22 ], the Fourier amplitude sensitivity test (FAST) [ 23 , 24 ] and derivative-based global sensitivity measures [ 25 , 26 ] have been developed to study complex models. The FAST method is a well-known global sensitivity approach.…”