## 5 Conclusions  
The work presented introduces an automatic calibration procedure and compares it against a manual process to evidence differences, advantages and disadvantages by applying both of them to a dynamic simulation model of a real office building in Rome. The methodology for automatic calibration includes SA for selecting the most relevant parameters and simulation-based optimization to minimize the difference between the simulated and measured data, automatically performed coupling of IDA ICE and MatLab and IDA ICE and GenOpt, respectively. It has to be considered that the main calibration activity was pre-eminently focused on the envelope, in some part on the plant and marginally on the equipment. Although occupancy and lightning could be generally relevant for calibration procedures, they were not considered in this analysis since they were not influential in this particular study. So, in principle, the results of this paper could be considered valid for cases in which these parameters can be neglected. Both manual and automatic calibrations show good results in terms of accuracy and a physically plausible representation of the case study, although automation presents finer tuning results, even in a reduced time frame. For instance, referring to the envelope calibration, the manual procedure gave an RMSE of 0.35 \({}^{\circ}\)C and 0.62 \({}^{\circ}\)C for calibration A (first floor) and calibration B (basement) respectively, while results for the automatic procedure showed an RMSE of 0.27 \({}^{\circ}\)C and 0.42 \({}^{\circ}\)C. Concerning heating system calibration, an RMSE of 0.54 \({}^{\circ}\)C and 0.39 \({}^{\circ}\)C resulted respectively from manual and automatic processes. Automatic calibration results in a better prediction of energy consumption since the comparison with real consumption bills produces a BE of 2.11% and a CVRMSE of 8.37% versus a BE of -7.69% and a CVRMSE of 9.66% for the manual process. Nevertheless, automatic calibration appears still strictly linked to the operator expertise, since the quality of the results depends on the settings and monitoring of the optimization procedure.  
Figure 17: Delivered energy from manual and automatic calibrated models compared with real consumption from bills.  
Figure 18: Scatter plot of delivered energy from manual and automatic calibrated models compared with real consumption from bills.  
**Author Contributions:** Conceptualization, C.C. and F.B.; methodology, C.C.; software, V.A.P.; investigation, F.B. and M.L.; data curation, M.L.; writing--original draft preparation, F.B.; writing--review and editing, F.B. and C.C.; supervision, C.C.; project administration, L.D.S.; funding acquisition, L.D.S.  
**Funding:** This research has been carried out within the \"Renovation of existing buildings in NZEB vision (nearly Zero Energy Buildings)\" Project of National Interest (Progetto di Ricerca di Interesse Nazionale-PRIN) funded by the Italian Ministry of Education, Universities and Research (MIUR), grant n. 2015S/E247, coordinator [PERSON] was supported by a contract within the PRIN framework.  
**Acknowledgments:** The authors wish to acknowledge CIRM for sharing the energy data and providing access to the building for the survey and the monitoring activity.  
**Conflicts of Interest:** The authors declare no conflict of interest.