## 6 Results and Discussion
### Dynamic Energy Simulations under Historical and Present Climate Conditions  
Considering the energy efficiency of the newest part of the building and the different sources of energy, the simulations conducted here simultaneously incorporate the sections built in 1976 and 2007 into one scenario and only the 2016 section in another. This approach aims to compare gas consumption for older buildings with electricity production/requirements for new ones. The energy consumption, expressed on both monthly and annual scales, is assessed in kWh/m2. Examining the seasonal and annual gas consumption, depicted in Figures 8 and 9, respectively, it is noteworthy that, as expected, the 1976 building exhibits higher energy requirements than the newer building. Specifically, monthly consumption ranges from 29% (in April) to 54% (in November), assuming the MM5 climatic database surpasses the other section. Annually, a similar discrepancy is anticipated between the two buildings, with a maximum value of +50% predicted using CORDEX data. The comparison of estimates derived from different climatic inputs underscores the impact of these parameters on the predictions. When considering the SIRMIP dataset as a reference, given its reliance on measured values, the DB dataset tends to significantly overestimate global monthly consumption, particularly from January (+46%) to April (+88%). This overestimation arises from the higher average temperatures assumed, leading to increased energy demands for maintaining comfortable conditions. Similar discrepancies are observed with the CORDEX dataset, which markedly over-calculates energy consumption, especially in February (+110%), March (+88%), and April (+199%). For the remaining months, the dataset underestimates from \(-33\%\) in November to \(-1\%\) in December. It is important to note that this dataset consists of 3-hour estimations and, consequently, may not accurately capture temporal fluctuations. The MM5 regional climate model, in contrast to the others, aligns more closely with the SIRMIP consumption, except for April, which shows a significant deviation (approximately \(-70\%\)). Extending the analysis to the annual scale (Figure 9), the observed trend remains consistent. The MM5 dataset continues to demonstrate more accurate performance compared to the historical DB dataset and the other virtual anemometer, with an error of +3%. However, more substantial variations are expected with the DB and CORDEX databases, which predict higher gas consumption of about +51% and +52%, respectively. Based on these results, the general tendency to adopt DB weather files for building evaluation and calibration reveals limitations, mainly because the data are historical and not representative of a specific current year. In addition to the considerations discussed in the earlier parts of the case study, evaluating the impact of different climatic files on an energy-efficient NZEB building is valuable. Here, the energy requirement is assessed based on electricity consumption per square metre. The chart shown in Figures 10 and 11 indicates that the energy requirement of the newest section of the building is significantly lower than that of the older sections. Specifically, the newest part uses an average of 3.43 kWh/m\({}^{2}\) per month, compared to 17.93 kWh/m\({}^{2}\) for the 1976 section and 9.29 kWh/m\({}^{2}\) for the 2007 section. Moreover, the NZEB building demonstrates less sensitivity to different climatic files, showing minor variability across the analysed datasets. The DB file generally overestimates the building's energy performance, while the MM5 dataset consistently proves to be the most accurate in representing real conditions. In contrast, CORDEX mostly overestimates energy consumption. On an annual scale (see Figure 11), the differences in both energy generation and consumption become less pronounced.