## 3 Results and Discussion
These findings emphasise the necessity of refining material parameters, particularly those related to the moisture content in wooden structures, and improving environmental modelling of solar gains and air infiltration. While computational models effectively capture long-term trends, their reduced sensitivity to short-term fluctuations suggests potential applications in optimising energy control strategies, particularly in heating system design. The following findings should be emphasised:  
1. Empirical temperature-in-time development demonstrates the quasi-cyclic running, corresponding to daily warming-up, followed by nights of cooling-down. 2. Similar trend in both datasets is indicative of the applicability of the simplified computational model to the prediction of basic thermal technical processes in building structures. 3. Most perceptible differences between both datasets correspond to the less smooth course of empirical data. 4. An approximate difference between both datasets is 0.7 K; maximal values are greater than 1.5 K.  
From time to time, the peaks in temperature development from computational simulation occur sooner or later than in the empirical data. This can indicate some model restrictions in representing the dynamics of thermal transfer or reactions to unexpected changes in the environment. The following groups of reasons for the above-specified differences in datasets can be distinguished:  
1. Environmental influences: Local structural manifestations, such as thermal bridges, behaviour of residents, or variability of exterior weather conditions, cannot be incorporated into a reasonable deterministic model. Solar gains through windows, unexpected ventilation, etc., can significantly influence temperature development, but their reliable modelling is also quite difficult. 2. Material properties: Timber structures are rather complicated systems with heterogeneous and anisotropic material properties. Model assumptions on thermal conductivity and capacity, as well as on moisture content, rarely perfectly correspond to real conditions. 3. Model simplifications: Numerical models rely on a set of simplifications, to come to robust and effective computations. For example, all assumptions on (quasi-)stationary states neglect intermediate phenomena such as ventilation caused by door or windows opening; detailed analysis of such forced air flow in rooms is not realistic. 4. Measuring Equipment and Calibration: The recorded data are influenced by the accuracy and resolution of the measuring equipment used. Even minor errors in measurement or calibration could have contributed to deviations. For example, the effects of local thermal bridges or solar gains were not measured uniformly throughout the experiment. 5. Impact of Moisture in Structures: Timber structures are sensitive to changes in moisture, which affects their thermal properties, such as thermal conductivity and capacity. The simulation assumed constant material properties, whereas, in reality, moisture levels could fluctuate significantly, depending on temperature, seasonal changes, and microclimatic conditions. In the model, these variables could not be fully accounted for. 6. Influence of Internal Heat Gains: The simulation did not consider internal heat gains from occupants, electrical appliances, or lighting. These factors could have influenced the temperatures measured, especially during the day when internal heat gains are more significant. While these simplifications were intentionally made to focus on fundamental thermal processes, they may have contributed to discrepancies between the data. 7. Limitations of the gbXML Format for Data Transfer: The gbXML format, used for transferring data from BIM to BEM, does not convey all details regarding the thermal-technical properties of individual construction layers. For example, information about the actual composition of insulation layers or surface materials may be simplified during export, affecting the accuracy of the simulation.