## 3 Results
### Variation of the Specific Parameters in the Model  
When modeling, it is necessary to have a lot of data directly related to the use of the building. They need to be obtained from the statistics for the type of building concerned, or to be chosen from standard or prescribed values. In the case of our validated model these data were in full accordance to the values obtained from monitoring the offices. In the continuation of the study, we focused on determining some parameters that would be generalized and suitable for use in similar models. In this sense, a wider group of simulations with variations of an individual parameter was performed in order to define the most appropriate parameter. The analysis included an evaluation of how much the variation of these parameters affects the final results, the energy use and the average indoor air temperature of the offices, compared with the results of the validated model. The basis for all the variations was therefore the validated model. The description of the variations of the validated model used to obtain the generalized parameters is given in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**Energy**} \\  & **Parameter** & **Variation** & \multicolumn{2}{c}{**(\&\) **(\&\) **(\&\)**(\&  
The first simulation set of parameters (S-1 to S-3) refers to the use of external shading devices. For the initial simulation a shading-coefficient assessment was made (Table 3), which was essentially a simplification of the actual use of external shading devices in each individual office and was for this purpose the same for all the windows. For the simulations that followed, two uniform shading coefficients, as a simplification of fixed shading, were used throughout the whole year, i.e., 0.80 and 0.60, respectively. Among the variations with shading devices, the results of the S-1 variation with an estimated common shading coefficient for all the windows of the model (Table 2) and the variation S-2 with a uniform shading coefficient of 0.8 best matched the results of the validated model. In the first case only 2.4% less energy for heating and cooling was needed, while in the second case 2.5% more energy compared to the validated model was needed. The results for the energy use of the S-3 variation were more unfavorable, because the total energy use was 10.1% higher than the one in the validated model. Similarly favorable results were shown for the average indoor air temperatures: variations S-1 and S-2 were very close to the validated model, while in S-3 the temperatures differed (Figure 3). The next set of parameters (W-1 to W-3) that we considered in the simulations was related to the strategies of window opening. The parameters included no window opening (W1), scheduled window opening in the morning between 08:00 and 09:00 in the cooling season only (specific months: April, May, June, September) (W-2) and window opening in the morning between 08:00 and 09:00 all year round (W-3) (Table 2). The schedule was based on the actual monitored opening of users, which was most common in the morning, after the user's arrival at work. The comparison of the energy results showed that in this case, the variation W-2 was the one which best matched the results of the validated model. Variation W-2, with manual opening between 08:00 and 09:00 in the morning during the cooling months, provided 1.8% lower results for energy use in the heating season and 0.9% higher results in the cooling season, which resulted in 0.3% lower total energy use compared with the validated model. Also, the average air temperatures in this particular variation were the closest to the temperatures of the validated model (Figure 4). Two additional important parameters were assessed with the help of simulations: users' presence (and the related operation of devices) and the use of artificial lighting in the offices.