Influence of weather natural variability on the thermal characterisation of a building envelope

TL;DR

This paper introduces a numerical method to evaluate how weather variability affects the accuracy of thermal resistance measurements of building envelopes, emphasizing the importance of weather conditions in non-intrusive testing.

Contribution

It presents a novel stochastic approach using synthetic weather data to assess the repeatability and sensitivity of thermal resistance estimates under varying weather conditions.

Findings

11 days needed for robust thermal resistance estimation

Outdoor temperature and wind speed are highly influential

Weather variability significantly impacts measurement accuracy

Abstract

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.