Thermal Modeling of a Historical Building Wall: Using Long-Term Monitoring Data to Understand the Reliability and the Robustness of Numerical Simulations

Abstract

Thermal modeling of building components plays a crucial role in designing energy efficiency measures, assessing living comfort, and preventing building damages. The accuracy of the modeling process strongly depends on the reliability of the physical models and the correct selection of input parameters, especially for historic buildings where uncertainties on wall composition and material properties are higher. This work evaluates the reliability of building thermal modeling and identifies the input parameters that most affect the simulation results. A monitoring system is applied to a historic building wall to measure the temperature profile. The long-term dataset is compared with the result of a simulation model. A sensitivity analysis is applied for the determination of the influential input parameters. A two-step optimization is performed to calibrate the numerical model: the first optimization step is based on an optimized selection of the database materials, while the second optimization step uses a particle swarm algorithm. The results indicate that the output of the simulation model is largely influenced by the coefficients describing the coupling with the boundary conditions and by the thermal conductivities of the materials. Very good results are obtained already after the first optimization step (RMSE=0.75 °C) while the second optimization step improves further the agreement (RMSE=0.48 °C). The parameter values reported in the datasheets do not match those found through optimization. Even with extensive optimization using an algorithm, starting with monitoring data is insufficient to identify material parameter values.

Identifying key parameters through a sensitivity analysis for realistic hygrothermal simulations at wall level supported by monitored data

“…Although it is more straightforward than other approaches, the calibration with the parametric method gives a result very close to the experimental data. Moreover, past analyzes [24] have also shown that this approach provides a good calibration. In fact, in [24], it was seen that calibration with more sophisticated optimization methods (e.g.…”

“…Sensors were installed on the external and internal surfaces, as well as within the wall, during the renovation, as shown in Figure 2. Detailed information on the monitoring system can be found in [23,24]. On the outside, a pyranometer (Hukseflux SR05), a driving rain gauge (SMT -RS1119), a surface temperature sensor (thermocouples), and a temperature and humidity sensor (E + E EE060) were installed.…”

“…Since the heat storage function is equally related to the values of density and specific heat capacity, both parameters have been considered as a unique parameter. Their product, denoted by 𝐶 𝑣𝑜𝑙 [𝐾𝐽/(𝑚 3 𝐾)], has been taken into account (also considered in [24]).…”

“…Several studies have tried to calibrate hygrothermal simulations [16][17][18][19][20][21][22][23]. Calibrating a model that describes only heat transport is already challenging [24,25], whereas, when it comes to the hygrometric part, there is a greater number of parameters that need to be considered [26]. In the literature, numerical model calibration is often carried out using laboratory scale samples [19,22,27].…”

Long-term monitoring data of hygrothermal conditions of a retrofitted historic building in Settequerce, South Tyrol (Italy)

“…The monitoring system captured climatic conditions over a span of five years, providing continuous measurements both inside and outside the building, as well as within the retrofitted wall at three different depths. Further details on data collection methodologies are elucidated in references [1] , [2] , [3] , [4] , [5] . Data format Raw Analyzed Description of data collection Data were collected every five minutes from varying conditions within and around a retrofitted historic building over a span of five years.…”