## 1 Introduction
To calculate the impact of different parameters on the system or to define the sensitivity of the system, the variations in the building or user-related parameters are included in the building simulation models [27; 28; 29]. In some cases, even the meteorological parameters are involved [30]. To analyze such systems a very complex sensitivity analyses can be used, usually in combination with uncertainty techniques. The sensitivity analyses can also be applied to understand the relationships between the input and output variables in a model. In his work on a nearly-zero-energy building (NZEB), [PERSON] [28] systematically investigated the impact of three sets of macro parameters using differential sensitivity analyses (DSA), and then linked the results to the costs. He found that all the building system sizes and the overall initial investment costs in an NZEB are most sensitive to the indoor temperature set-point. Similarly, a very extensive analysis was performed by [PERSON] [31], who, using sensitivity analyses on selected buildings, investigated whether the same set of parameters was relevant to the user. In the case of many input variables, it is advisable to first perform sensitivity analyses and then uncertainty analyses. Namely, sensitivity analyses can significantly reduce the computational costs by selecting key factors that affect the energy performance of a building [32]. In highly complex systems, it is often necessary to perform comprehensive sensitivity analyses, which may also be multi-stage sensitivity analyses with design optimization [27]. In our case, the number of variables was limited to the measured data related to specific events in real offices, such as window opening, use of internal blinds and adjustments of temperature setting. This somehow simplified the parameter-impact study, but on the other hand, these measured data can represent valuable information about selected details that were investigated in the study. The purpose of this paper is to analyze the effect of various parameters on the simulation results from a TRNSYS model that was used to replicate real offices, as far as possible. The parameters were mainly associated with the users' presence and behavior when using the premises. They were obtained from measurements made on offices that were pilot demonstrators in the EU project COST EFFECTIVE (Resource- and Cost-Effective Integration of Renewables in Existing High-Rise Buildings, 7 FP, 2008-2012) [33]. The parameters were used in a TRNSYS model that was based on the actual technical performance of the existing offices equipped with radiant heating/cooling ceiling systems. The TRNSYS model was validated by a comparison with the results of the measurements [1; 34; 35]. Our first objective was to compare how the particular parameters that were obtained from the measurements influence the final values of the simulated energy use and to what extent they affect the average indoor air temperatures of the validated numerical model. The second goal was to establish how certain parameters related to the use of the building can be generalized and how much they affect the matching with the results of the validated numerical model by means of a comparative analysis.