A novel method for the estimation of thermophysical properties of walls from short and seasonal-independent in-situ surveys
Virginia Gori, UCL
Several studies have shown a performance gap between the published thermophysical properties (e.g., U-value) of building materials and those estimated from in-situ measurements of heat flux and temperatures. The performance gap has clear consequences on the decision-making and cost-effectiveness of energy-efficient measures; therefore a widespread use of thermophysical properties estimated from surveyed data would be preferable. However, steady-state methods usually adopted for the calculation of U-values from monitoring campaigns have several limitations (e.g., the length and seasonality of the recording period) that may prevent an extensive use of real-world data, for example as inputs for energy needs simulations. This thesis builds on and substantially contributes to the development of a novel dynamic Bayesian-based method (http://dx.doi.org/10.1016/j.enbuild.2014.04.004 , recently developed at the UCL Energy Institute), implementing new models and algorithms for the estimation of the thermophysical properties of a building element from short and seasonal-independent time series collected in-situ. The analysis explores the influence of exogenous (e.g., solar radiation and wind speed) and endogenous (e.g., inhomogeneities in the building structure, moisture) factors on the parameter estimation as well as their mutual influence on the overall thermal performance of building elements.
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