## 3 Results
### Effect of Six Specific Parameters Included in the Validated Model  
When modeling the thermal performance of a building, there is usually a lack of specific detailed information. The situation in our case was the opposite, since we obtained a lot of data from the long-term monitoring that we carried out in the offices. These data were analyzed and the known values were used in numerical simulations to optimize and validate the numerical model, which was complex since it included all the important elements and parameters. The validated numerical model was then used as a basis for the analysis of the effect of six parameters on the energy use and on the indoor air temperatures in the offices if these parameters are excluded from the simulation. The parameters were mainly related to the user's behavior in the offices, with only a few of them being attributed to the consequences of technical issues. These six parameters were as follows: intensive internal gains, additional extraction of energy with exhausted air from the mechanical ventilation system due to temperature differences, insufficient energy supply for cooling due to stopping the operation of the cooling system in the summer, window opening by the occupants, use of internal roller blinds by the occupants and temperature adjustments using room controllers by the occupants. The effect of their exclusion from the model was observed both seasonally and annually. The simulations were made one by one so that the specific parameters were individually excluded from the validated model (Table 1, rows A to F). Table 1 also shows the results for the validated model with all the specific parameters included (first row) and also for the validated model from which all six parameters are excluded (row G). The results of the analyses, the effect of each individual specific parameter excluded from model and the combined effect of all six of them, are presented in Figure 3 in comparison with the results from the simulation on the validated model. The first analyzed specific parameter was the unexpected and large internal gains in Office 4, which occurred in March of the monitored year. The analysis of the monitored data revealed that an extra expert was working in this office for 6 days; he was using a supplementary computer and a 3D scanner, which caused a noticeable amount of heat gain in the office. These additional internal gains, which were large but short term, were included in the model when optimizing it for the validation. However, when excluded from the model, they have a relatively moderate seasonal effect on the energy use, i.e., 2.3% in the heating season. If they had been observed with regards to the annual energy level, their influence would have been relatively small: according to the results of the simulations, 1.0% more energy is needed for heating per year if these additional internal gains are excluded from the model. Also, the calculated average indoor air temperature based on the simulations showed practically the same values for the heating season as with the validated model (Figure 3). A very similar situation also applies to the extraction of energy from the exhausted air produced by the mechanical ventilation due to unpredicted air-temperature differences in the offices. This parameter was included in the optimized model based on the analysis of simple measurements of the gradient of air temperatures in all the offices. The energy effect of the extraction of this extra heat produced by the exhausted air flowing through the air outlets of the mechanical ventilation system in the cooling season was significant. The simulations showed that if this parameter was excluded from the model, 7.0% more energy was used for the cooling, which confirms that the air temperatures in the upper air layers of the rooms, close to windows, can be considerably higher than in the other locations in an office. During the heating season, however, the above-mentioned air temperature differences were relatively small.