Impact de la variabilité des données météorologiques sur une maison basse consommation. Application des analyses de sensibilité pour les entrées temporelles.

摘要

This thesis is part of the ANR project FIABILITE dealing with the reliability of dynamic thermal simulation softwares and particularly with the potential sources of bias and uncertainties in the field of thermal and energy modeling of low consumption buildings. The solicitations such as the occupancy schedules, the weather data or the usage scenarios are among the most uncertain and potentially most influential inputs on the performance of a low energy building. To ensure the efficiency of such buildings, we need to determine the outputs dispersion associated with the uncertainty of the temporal inputs as well as to emphasize the variables responsible for the dispersion of the output in order to design the building in a robust manner. To address this problem, we have used the sensitivity indices of Sobol adapted to complex models with high dimensions, such as building models for dynamic thermal simulations. The management of the functional inputs being a lock for the scientific methods of standard sensitivity analysis, an innovative methodology was developed in the framework of this thesis in order to generate consistent samples with the estimate of the sensitivity. Although the method can incorporate generic functional inputs, it has been validated in this thesis using meteorological data and especially the typical meteorological year (TMY files) used in dynamic thermal simulations. The two main aspects of this development work lie in the characterization of the variability of meteorological data and the generation of samples to estimate the sensitivity of each weather variable dispersion on the thermal and energy performances of a building. Through various case studies derived from the thermal model of a low-energy house, the dispersion and influential parameters for meteorological variability are estimated. Results show a large range of uncertainties in the energy requirements from about 20 % at a confidence level of 95%.