# Urban Overheating Impact: A Case Study on Building Energy Performance
## 3 Results and Discussion
### Effects of Different Climatic Scenarios on BES
Table 3 summarizes the simulation results in terms of heating and cooling annual energy needs, using the six different climate datasets. Comparing the data shown in Table 3, significant differences can be noted both in winter and summer. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Climatic Data** & **Heating (kWh/m2)** & **Cooling (kWh/m2)** \\ \hline Rome 2019 & 70.4 & 47.7 \\ \hline Fiumicino 2019 & 88.1 & 25.4 \\ \hline Ciampino 2019 & 93.8 & 32.1 \\ \hline UNI 10349 & 91.8 & 31.1 \\ \hline TMY Fiumicino & 93.9 & 23.0 \\ \hline TMY Ciampino & 104.4 & 24.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sample building energy demands. Figure 9: Sample year and UNI 10349 wind speed and relative humidity comparison. In order to improve the data readability and comparability, the results listed in Table 3 are also reported as a histogram chart in Figure 10.  
Using the climatic conditions observed in Rome in 2019, it is possible to notice the lowest values in terms of energy needs for heating, equal to 70.4 kWh/m\({}^{2}\), and the highest for cooling, equal to 47.7 kWh/m\({}^{2}\), highlighting that the UHI phenomenon in Rome led to overheat when compared to surrounding areas, which is an important aspect in assessing the buildings' energy demand. Table 4 shows the percentage differences obtained for heating and cooling energy needs by comparing the simulation results with the climatic data of the sample year (Rome 2019) to those obtained with the other climatic data. By comparing the results in terms of heating energy demands, it is possible to observe percentage differences ranging from \(-20.1\%\)(Fiumicino 2019) to \(-32.5\%\)(TMY Ciampino). On the other hand, analyzing the percentage difference related to the cooling energy needs, a much wider range can be observed, from +48.7% (Ciampino 2019) to +107.0% (TMY Fiumicino). Starting from this results overview, it is possible to analyze the outcomes listed in Table 4 in two ways. Considering climatic data related to 2019 and taking as reference Fiumicino, it is worthwhile to observe that climatic conditions outside or inside the city influenced the heating and cooling energy demands, with percentage differences equal to \(-20.1\%\) and +87.5%, respectively. These differences allow us to understand the crucial role of meteorological data and their effects on predictions. Modeling a building with airport climate data leads, at best, to percentage differences of \(-24.9\%\) for heating and +48.7% when they are collected from the Ciampino weather station. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Climatic Data**} & \multicolumn{2}{c}{**Percentage Difference (\%)**} \\ \cline{2-3}  & **Heating** & **Cooling** \\ \hline Rome 2019 vs. Fiumicino 2019 & \(-20.1\) & +87.5 \\ \hline Rome 2019 vs. Ciampino 2019 & \(-24.9\) & +48.7 \\ \hline Rome 2019 vs. UNI 10349 & \(-23.3\) & +53.3 \\ \hline Rome 2019 vs. TMY Fiumicino & \(-25.0\) & +107.0 \\ \hline Rome 2019 vs. TMY Ciampino & \(-32.5\) & +94.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage differences in energy demands. Figure 10: Heating and cooling annual energy needs under different climatic conditions. Taking into consideration typical meteorological years such as those provided by the UNI 10349 and the IGDG collection, greater differences can be observed. The comparison between 2019 results and the TMYs allowed us to obtain differences ranging between \(-23.3\%\) and \(-32.5\%\) for the heating energy demand. This comparison related to the cooling energy need allowed us to achieve percentage differences ranging from \(+53.3\%\) to \(+107.0\%\). This comparison highlights the issue of using specific climate data. The problem is, however, related to the aim of simulations; if it wants to be predictive or if it must be a simulation of a calibrated model for an energy retrofit.