Influence of natural weather variability on the thermal characterisation of a building envelope. (15th April 2021) - Juricic, Sarah; Goffart, Jeanne; Rouchier, Simon; Foucquier, Aurélie; Cellier, Nicolas; Fraisse, Gilles
Abstract: The thermal characterisation of a building envelope is usually best performed from on-site measurements with optimised controlled indoor conditions. Conversely, occupant-friendly measurement conditions provide less informative data. Notwithstanding occupancy, the boundary conditions alone contribute to a greater extent to the energy balance, which implies that non-intrusive conditions bring into question the reproducibility and relevance of such measurement. This paper proposes an original numerical methodology to assess the reproducibility and accuracy of the estimation of the overall thermal resistance of an envelope under variable weather conditions. A comprehensive building energy model serves as reference model to produce synthetic data mimicking non-intrusive conditions, each with a different weather dataset. An appropriate model is calibrated from the synthetic data and provides a thermal resistance estimate. The accuracy of the estimates is then assessed in light of the particular weather conditions used for data generation. The originality also lies in the set of weather data that allow for uncertainty and global sensitivity analyses of all estimates with respect to six weather variables. The methodology is applied to a one-storey house reference model, for which thermal resistance is inferred from calibrated RC models. Robust estimations are achieved within 11 days. The outdoor temperature and the wind speed are highly influential because of the large air change rate in the case study. Highlights: Thermal characterisation of a building envelope influenced by weather conditions. Original methodology assesses minimal measurement duration for robust estimation. Thermal characterisation by RC models is accurate and faster than steady-state methods. Necessary 11-day measurements for robust estimation sets new benchmark value in field. Estimations highly influenced by outdoor temperature and wind speed in case study.