  The thermal characterisation of a building envelope is usually best performed
from on site measurements with controlled heating power set points.
Occupant-friendly measurement conditions provide on the contrary less
informative data. Notwithstanding occupancy, the boundary conditions alone
contribute to a greater extent to the energy balance. Non intrusive conditions
question therefore the repeatability and relevance of such experiment.This
paper proposes an original numerical methodology to assess the repeatability
and accuracy of the estimation of an envelope's overall thermalresistance under
variable weather conditions. A comprehensive building energy model serves as
reference model to produce multiple synthetic datasets. Each is run with a
different weather dataset from a single location and serves for the calibration
of an appropriate model, which provides a thermal resistance estimate. The
estimate's accuracy is then assessed in the light of the particular weather
conditions that served for data generation. The originality also lies in the
use of stochastically generated weather datasets to perform an uncertaintyand
global sensitivity analysis of all estimates with respect to 6 weather
variables.The methodology is applied on simulated data from a one-storey house
case study serving as reference model. The thermal resistance estimations are
inferred from calibrated stochastic RC models. It is found that 11 days are
necessary to achieve robust estimations. The large air change rate in the case
study explains why the outdoor temperature and the wind speed are found highly
influential.
