## 4 Comparison between Simulation and Experiments  
The hygothermal simulation was compared with the experimental results. The well-known and widely validated computer simulation program WUFI Pro 5.3 (Fraunhofer Institute for Building Physics, Holzkirchen, Germany) [23; 39] was used to evaluate the hygrothermal behaviour of walls. WUFI Pro considers coupled heat and moisture transfer through the assembly and complies with the EN 15026 standard [40]. To reproduce the experimental protocol described in Section 3.1, the partition walls were set to a temperature of 23 \({}^{\circ}\)C and a relative humidity of 50%, and the simulation was performed until a steady state was reached. Then, external and internal temperatures were fixed at 20 \({}^{\circ}\)C and the final moisture content profile was adopted as the initial boundary conditions for further simulation, where the humidity was changed cyclically between 50% and 80% every 12 h for five days. No sun, rain or wind were introduced into the artificial climate file. Comparisons between simulation and experimental results for each assembly are shown in Figure 9.  
Figure 8: Moisture buffer values for individual materials.  
Figure 7: Measured changes in mass of individual materials during the adsorption and desorption phases over five 24-h cycles.  
We note that there are discrepancies between the results. In the initial cycles, the simulation results underestimate the adsorption process, but subsequently the situation is reversed. The simulation results underestimate the adsorption process and overestimate the desorption process, but the data from the simulations follow the same trend as the experimental data.  
Numerous studies report similar observations [19; 41; 42; 43]. These differences are usually assigned to uncertainties in input material data and boundary conditions, especially in the case of highly hygroscopic bio-based or wood-based materials made of natural products, whose properties may vary slightly from one sample to another. In the literature, we find several studies reporting that deviations appear when the samples are subjected to a rapidly changing dynamic load [22; 43]. The key role is assigned to the sorption isotherm and the vapour permeability, which should be estimated via a dynamic method instead of the standard steady-state method.  
Additionally, in order to quantitatively compare the discrepancy between the model and the measurements, a root mean-square error (_RMSE_) correlation coefficient was calculated, using the following equation:  
\[RMSE=\sqrt{\sum_{i=1}^{N}(x_{meas}-x_{model})^{2}/N}, \tag{9}\]  
where \(X_{meas}\) are the measured data, \(X_{model}\) are the modelled data and \(N\) is the number of data points.  
The value of RMSE is 0.86 for Assembly A, 0.64 for Assembly B, 1.09 for Assembly C and 0.81 for Assembly D; this shows that there is a small discrepancy between the modelled and measured results. Moreover, a linear regression was used to calculate a correlation factor between the measured and simulated values, as shown in Figure 10. The correlation factors were 0.85 for Assembly A, 0.91 for Assembly B, 0.91 for Assembly C and 0.97 for Assembly D. The correlation is very good; the factors indicate that the simulated results calculated by the software are consistent with the measured data. The accuracy of the simulated results is sufficient to enable use of the simulation method to study the moisture-buffering effect in practical applications.  
Figure 9: Comparisons of the numerical and experimental data, showing the change in mass of individual assemblies over five 24-h cycles: (**a**) Assembly A; (**b**) Assembly B; (**c**) Assembly C; (**d**) Assembly D.