## 3 Results and Discussion  
For each thermal zone, the heating demand was obtained for the base scenario, i.e., the simulation of the full operation of the building, where all apartments are heated with a fluctuating set-point temperature determined as the mean weekly temperature measured by the temperature sensors installed in the dwellings. Figures 4-6 show the hourly distribution of the heating load for the analyzed dwellings obtained by the dynamic simulation, each representing a macro thermal-zone resulting from the summation of  
Figure 3: 3-D model of the building case-study. the thermal zones simulating the dwellings. For simplicity reasons, the graphs show only the heating season (i.e., from 1 st January to 15 th April, and from 1 st November to 31 st December). The annual heating consumption was calculated as the area below the power curve obtained by the simulation. Both the estimated and the measured energy consumptions were normalized with respect to the reference HDDs of the building location (1911) as reported by Equations (1) and (2). \[\left(normalized\;consumption\right)_{estimated}=\frac{estimated\;consumption}{ HDD_{(wather\;file)}}.1911 \tag{1}\]  
Figure 4: Simulated heating load of apartments 1C, 2A and 3B. Figure 5: Simulated heating load of apartments 4C, 5A and 6B. Figure 6: Simulated heating load of apartments 7C, 8A and 9B. \[\left(normalized\ consumption\right)_{measured}=\frac{measured\ consumption}{HDD_{2017-18}}\cdot 1911 \tag{2}\]  
where \(HDD_{(wather\ file)}\) are the HDDs calculated for the weather file used for the simulation and \(HDD_{2017-18}\) are the HDDs of the heating season of 2017-2018 of the building location obtained by the local weather database. A calibration of the model has been performed, aimed at decreasing the magnitude of the simulation error in some apartments. In fact, after the first simulation, apartments 5A, 6B, 7C and 8A showed the highest simulation errors (see Table 3). In this regard, information was retrieved by the authors in the field, during meetings specifically organized to collect data about end-user's behavior. In particular, it has been found that users of those apartments used to open their windows during the heating hours, instead of employing the TRVs, to decrease the indoor temperature. For this reason, for the above-mentioned apartments, a calibration has been performed by the authors by increasing the ventilation rate until the error decreased to a range of \(\pm 15\%\). Results are reported in Table 3, namely: the normalized measured energy consumption of each dwelling for the heating season 2017-2018 (estimated basing on the reading of the HCAs); the normalized estimated energy consumption (i.e., obtained by means of the dynamic simulation implemented using TRNSYS17), respectively before and after the calibration process (BC and AC); the error between the estimated and the measured normalized thermal energy consumption before and after the calibration process (BC and AC), calculated as per Equation (3). \[error\ [\%]=\frac{estimated\ consumption\ - measured\ consumption}{measured\ consumption}\cdot 100 \tag{3}\]  
In Table 3, only the energy consumptions normalized with respect to the reference HDDs of the building location are reported. As shown in Table 3, following the calibration process on dwellings 5A, 6B, 7C and 8A, the error calculated with respect to the total energy consumption of the building decreased to \(-2\%\), while the one of single dwellings decreased to a range of \(+6/-12\%\). Thus, the validation results have been considered to be acceptable for the purpose of the present analysis.