## 4 Results and Discussion  
In Figure 10, the number of defrost cycles are reported for every year considered in the simulations. The HP placed in Milan performs more defrost cycles than the units placed in Livigno and San Benedetto del Tronto. It can be observed that there is an important variability in the number of defrost cycles conducted from a year to another for all of the towns considered, and we should also indicate that the test reference year generally underestimates the number of defrost cycles performed by the units, as noted in Table 6.  
In Figure 11 the thermal energy demand of the building and the seasonal coefficient of performance as defined in Equation (10) are reported for different years and municipalities. The figure shows that for every considered case, the thermal energy demand is always higher when defrost is taken into account with respect to the case of negligible defrosting; on the contrary, the SCOP is lower when defrosting is taken into account. The variation in thermal energy demand from a year to another is relevant for all of the investigated municipalities: in particular, focusing on simulations that consider defrosting and refer to San Benedetto del Tronto, the thermal energy demand for 2013 is 6009 kWh, almost two times the thermal energy demand of 2020, which is equal to 3184 kWh. On the contrary, the seasonal coefficient of performance displays smaller variations from one year to another. In detail, in the defrost case, the SCOP assumes values in the range 2.86-2.99, with mean value 2.93; indeed, the percentage changes referring to the mean value are 2.4%/+2%. By neglecting the defrost cycles, the SCOP assumes values in the range 3.01-3.21, a displaying mean value 3.09 and percentage changes with respect to the mean value \(-2.8\%/+3.6\%\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Maximum** & **Minimum** & **TRY** \\ \hline Milan & 1595 (year2018) & 827 (year 2015) & 802 (CTI TRI) \\ Livigno & 1008 (year 2019) & 254 (year 2013) & 340 \\ San Benedetto del Tronto & 904 (year 2013) & 235 (year 2019) & 158 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Maximum, minimum, and TRY number of defrost cycles. Figure 10: Number of defrost cycles for the years considered in simulations. “TRY MN” refers to Meteororm TRY. Concerning the test reference year defined through Meteorom for San Benedetto del Tronto, the thermal energy demand of the building is overestimated in both cases, with or without defrost (thermal energy demand is lower with respect to the TRY predictions for 5 years out of 7); conversely, SCOP values obtained from the test reference year are in accordance with the values obtained from the simulations that considered real weather data from the different years. Considering Milan, Figure 11 shows, as for the case of San Benedetto del Tronto, important variations of the thermal energy demand from one year to another (considering defrost, minimum value of 9257 kWh for 2019 and maximum value of 14,009 kWh for 2013; neglecting defrost, minimum value of 8676 kWh for year 2019 and maximum value of 13316 kWh for year 2013). Slight fluctuations in the SCOP value are obtained, but these variations are smaller than in the case of San Benedetto del Tronto (defrost case SCOP range 2.72-2.83, neglecting defrost SCOP range is 2.84-2.94). Similar results are obtained for Livigno, with important variations in thermal energy from a year to another (range for defrost case 30,329 kWh-36,183 kWh, range neglecting defrost 29,496 kWh-35,685 kWh). In fact, for Livigno, sensible variations in the SCOP values (up to 5.6%, referring to SCOP mean value of the years 2013-2020) that are higher than for San Benedetto and for Milan (range for defrost case 2.46-2.71 range neglecting defrost 2.5-2.79) are achieved. Figure 11 as also shows that the Meteorom TRY for Livigno underestimates the thermal energy demand as well as the SCOP value.