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. Sensitivity analysis of complex models: coping with dynamic and static inputs. Reliability Engineering and System Safety, Elsevier, 2015, 134, pp.268-275 AbstractIn this article, we address the issue of conducting a sensitivity analysis of complex models with both static and dynamic uncertain inputs. While several approaches have been proposed to compute the sensitivity indices of the static inputs (i.e. parameters), the ones of the dynamic inputs (i.e. stochastic fields) have been rarely addressed. For this purpose, we first treat each dynamic as a Gaussian process. Then, the truncated Karhunen-Loève expansion of each dynamic input is performed. Such an expansion allows to generate independent Gaussian processes from a finite number of independent random variables. Given that a dynamic input is represented by a finite number of random variables, its variance-based sensitivity index is defined by the sensitivity index of this group of variables. Besides, an efficient samplingbased strategy is described to estimate the first-order indices of all the input factors by only using two input samples. The approach is applied to a buildPreprint submitted to Reliability Engineering and System Safety August 5, 2014ing energy model, in order to assess the impact of the uncertainties of the material properties (static inputs) and the weather data (dynamic inputs) on the energy performance of a real low energy consumption house.

International audienceUncertainty and risk analyses are important tools for building designs and performance assessment of renewable energy systems. This task requires to account for the variability of the weather data. In this work, we develop a methodology to characterize and simulate stochastic weather data. The stochastic features of each weather input, such as auto-correlation and hourly cumulative distribution functions, are extracted from the dataset at hand. Then, the procedure of Iman and Conover is used to generate stochastic weather inputs. The approach is applied to a sequence of 1 month extracted from the typical meteorological year of the city of Lyon, France. The simulated stochastic weather data are employed to perform the uncertainty and sensitivity analysis of a real passive, low-energy house. The results show that the uncertainty on the predicted energy needs is roughly 20% and is essential due to the stochastic variability of the out- door air temperature