## 1 Introduction
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Standard/Guideline**} & \multicolumn{2}{c}{**Monthly Limit (\%)**} & \multicolumn{2}{c}{**Hourly Limit (\%)**} \\ \cline{2-5}  & **MBE** & **CVRMSE** & **MBE** & **CVRMSE** \\ \hline ASHRAE Guideline 14 [17] & \(\pm\)5 & 10 & 10 & 30 \\ International Performance Measurement & & \(\pm\)20 & - & 10 & 20 \\ and Verification Protocol [18] & & \(\pm\)5 & 15 & 10 & 30 \\ Federal Energy Management Program [19] & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Limits of MBE and CVRMSE for the calibration of building simulation models defined by different standard/guideline. Manual calibration consists of iterative and pragmatic process that involves the fine tuning of the input variables in a trial and error procedure. It uses building characteristics data from audit, energy use data and zone monitoring to get insight into the physical and operational characteristics of the building. The objective is to minimize the difference between measured and simulated output variables such as energy consumption, gathered from audit processes or inside temperature trends, obtained through environmental monitoring. Graphical techniques are widely used in manual calibration, as in the development of systematic and evidence based models [23]. Manual calibration requires high skill and expertise of the modeler that modifies the input basing principally on its experience. Apart from skill, the process usually requires a certain amount of time to be completed. Usually, to better understand the process, the input variables are changed one at a time then the simulation is run and for each simulation the output has to be compared with the original model. Many studies in the literature adopt manual calibration [23; 24; 25; 26]. Manual calibration can also be used to gain detailed knowledge of the physical and operational characteristics of the buildings [27; 28]. For example, [PERSON] et al. 2016 [29] calibrated a model of a complex historical building and used manual calibration as a tool to identify various wall layers made by the superposition of unknown materials of different ages. Automatic calibration relies on mathematical and statistical methods that mostly use optimization functions to minimize the difference between measured and simulated data. Many automatic procedures also include sensitivity analysis to reduce the number of input to the optimization tool and speed up the computing time [13; 15; 30; 31; 32]. Various algorithms have been used for the automatic calibration, among them the Bayesian approach [33], pattern-based approach [34], evolutionary algorithms [35; 36] and particle swarm optimization [13; 16; 37]. In general, both automatic and manual calibration result either time consuming or costly methods. Manual calibration requires time of an experienced analyst while automatic calibration mainly requires computing power and time to complete the process. Therefore, it is difficult to determine whether one method could prevail over the other. Apart of a case in which the difference of the two approaches has been evidenced [38] the literature studies scarcely assess this issue. For this reason, it seems to the authors that the proposed evaluation, even if related to a specific case study, could be informative to the literature. This paper introduces an automatic calibration procedure developed by the authors that couples dynamic simulation and sensitivity analysis with automatic optimization using IDA ICE, Matlab and GenOpt respectively. This automatic calibration method has been compared to a manual procedure to evidence differences, advantages and disadvantages by applying both of them to a dynamic simulation model of a real office building in Rome, Italy. The comparison has been made in terms of accuracy of prediction of the real building consumption bills by the manual and automatic energy model calibrated using the temperature profile trends. Section 2 illustrates the methodology used to face the model calibration (both manual and automatic).