## 4 Dynamic Energy Simulations under Future Climate Conditions  
### Generation of Future Climatic Files for Dynamic Energy Simulations  
Future climatic files are generated by using the Coordinated Regional Climate Down-scaling Experiment (CORDEX). Sponsored by the World Climate Research Program (WCRP) to develop a coordinated framework for evaluating and improving Regional Climate Down-scaling (RCD) techniques, CORDEX produces worldwide fine-scale climate data [52]. The CORDEX results are assumed as a baseline by the Intergovernmental Panel on Climate Change (IPCC) to define climate change impact and adaptation studies. Its various domains allow the estimation of meteorological variables all over the world, and the Euro-CORDEX, reported in Figure 5, is its European branch. Figure 4: Schematisation of the investigated Multi-Storey (**a**), Shaft-Box (**b**), Corridor (**c**,**d**) and Box-Window (**e**) DSFs. The Aire Limitee Adaptation dynamique Developpement InterNational (ALADIN), used at the Centre National de Recherches Meteorologiques (CNRM) with the name CNRM-ALADIN and available in Euro-CORDEX was selected in the present work [53; 54]. This limited-area bi-spectral model was chosen due to its capability to predict with good approximation climatic parameters both spatial and temporal scales inside the European area [55; 56]. Thus, data with a 3 h time frequency were extracted from the nearest grid point to Pescara, which is 7.50 km from the case study, at 42\({}^{\circ}\)28\({}^{\prime}\)57\({}^{\prime\prime}\) northern latitude and 14\({}^{\circ}\)08\({}^{\prime}\)07\({}^{\prime\prime}\) eastern longitude. The selected RCP was 4.5, which is consistent with a future with relatively ambitious emission reductions, and this scenario was chosen for being in accordance with national policies centred on reducing greenhouse gas emissions. The available weather variables were relative humidity, atmospheric pressure, global solar radiation, wind velocity and temperature. The definition of the reference year was conducted according to the method described in the technical standard EN ISO 15927-42005 [57] and following its suggestions [52; 58]. Multiple years were extracted to obtain various typical years. The first step involved the calculation of the daily averaged value for each climatic parameter (\(p\)), month (\(mt\)) and year (\(y\)) of the datasets. Then, the averaged values for a specific month in all the available years were sorted in increasing order to calculate the cumulative function \(\phi_{(p,mt,i)}\) for each parameter and \(i\)th day using Equation (1). \[\phi_{(p,mt,i)}=k(i)/N+1 \tag{1}\]  
where \(k_{(i)}\) is the rank order of the \(i\)th day and \(N\) is the total number of days in a month across all years. The following step consisted of sorting the averaged values for a specific month and year in increasing order to obtain the cumulative distribution function \(F_{(p,y,mt,i)}\) for each parameter and \(i\)th day (Equation (2)). \[F_{(p,y,mt,i)}=I(i)/n+1 \tag{2}\]  
Then, for each month and year, the statistics by Finkelstein-Schafer were defined according to Equation (3). The last two steps involved the sorting of months, for which the rank was calculated for every parameter and summed to obtain the total ranking and for each month. Among the first three months with the lowest ranking sum, the one with the lower absolute deviation was chosen as a representative for the TMY generation. \[F_{(p,y,mt)}=\sum_{i=1}^{n}\left|F_{(p,y,mt,i)}-\phi_{(p,mt,i)}\right| \tag{3}\]  
To improve the quality of the generated TMY, weighted meteorological parameters were inserted in the Finkelstein-Schafer, as suggested by [PERSON] et al. [58]. The stronger influence of some variables than others is taken into account by increasing the weight of those parameters. Higher impact factors are attributed to surface temperature and solar radiation (8/24), whereas a lower value (4/24) is assigned to relative humidity and wind speed, which slightly affect energy simulations.