Characterization of the thermal structure of different building constructions using in-situ measurements and Bayesian analysis

A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error

“…Unlike the SWall case study, no correlation is apparent between the lumped thermal resistances, as the principal axes of the contours representing the posterior probability distribution are not rotated with respect to the Cartesian axes [60]. Specifically, the model found the thermal resistances to be independent of each other, and consequently their position in thermal resistance space is not correlated.…”

“…This suggests that the model derived a constant total R-value (and consequently U-value) of the wall, while the relative magnitude of each lumped thermal resistance could vary (e.g., a decrease in R 1 tended to be compensated for by an increase in R 2 ). Such relationships may be used to provide valuable insight into the thermal structure of the element; here the solid wall construction strongly constrains the total thermal mass and resistance, whilst the comparable thermophysical properties of the materials in the wall only weakly constrain the position of two effective thermal masses in thermal resistance space [60].…”

“…The full distribution of the thermophysical parameters of the 2TM model is shown in Figure 5. The corner plot shows a negative relationship among the thermal resistance estimates, as the principal axis of the contours representing the posterior probability distribution are rotated with respect to the Cartesian axes [60]. This suggests that the model derived a constant total R-value (and consequently U-value) of the wall, while the relative magnitude of each lumped thermal resistance could vary (e.g., a decrease in R 1 tended to be compensated for by an increase in R 2 ).…”

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

“…When operational data (measured sensor data) is unavailable and only thermo-physical characteristics are available, the parameters can be determined analytically [121,122], or by developing an analytical model as a reference model using available data and matching the resultant dynamics of the thermal network and reference models [74,80]. However, if there is good availability of measured data but a lack of thermophysical characteristics data, inverse methodologies [123][124][125] are applied to determine parameter values by minimizing the prediction error between the thermal-network model and the measured data [126][127][128][129]. We may categorize parametric identification methods into three groups based on the numerous methods provided in the literature:…”