## 6 Results and Discussion  
### Intercomparisons among Weather Datasets  
The selected historical and current climatic files are compared in terms of temperature, global solar radiation, atmospheric pressure, relative humidity, and wind speed and direction. The adopted reference pattern is derived from the SIRMIP meteorological mast. Figure 7 depicts the Taylor diagrams generated from both seasonal and annual data. Seasons are defined according to the Italian astronomical seasons: spring from 21/03 to 20/06, summer from 21/06 to 20/09, autumn from 21/09 to 20/12, and winter from 21/12 to 20/03. Figure 6: Comparisons among building annual gas consumptions for the year 2015–2016. Focusing on the annual dataset (Figure 7a), it becomes evident that temperature and global solar radiation are the climatic parameters better described by the investigated databases. The temperature values (blue markers in the chart) present a correlation coefficient ranging from 0.79 for the CORDEX dataset to 0.88 for the MM5 dataset. Additionally, good standard deviations and centred root-mean-square differences are calculated for this variable. A similar pattern is observed for solar radiation, which is well predicted by all sources, particularly by the DB dataset. Conversely, the two regional models exhibit similar but lower performance. Moreover, [PERSON] and [PERSON]'s approach provides estimations for this parameter with good correlation coefficients, standard deviations, and RMSD, respectively, equal to 0.81, 0.65, and 0.60. Atmospheric pressure (green markers in the chart) is only compared for the MM5 and CORDEX datasets because DB assumes a constant value equal to 101,181 Pa, rendering it incomparable with specific measured data. Among the two options, MM5 better describes the variability of this parameter with a correlation coefficient of 0.75, a standard deviation of 1.04, and an RMSD of 0.71. Conversely, CORDEX exhibits less accurate statistical parameters. Relative humidity and wind speed (light blue and yellow markers in the chart, respectively) are represented similarly by DB and CORDEX, although they demonstrate different predictive capabilities for the two variables. MM5 shows greater accuracy in estimating these parameters, with correlation coefficients of 0.43 for relative humidity and 0.23 for atmospheric pressure, along with standard deviations  
Figure 7: Normalised Taylor diagrams for an annual dataset (**a**) and for autumn (**b**), winter (**c**), spring (**d**), and summer (**e**). of 0.90 and 1.35 and RMSDs of 1.05 and 1.45, respectively. Lastly, wind direction (purple markers) is predicted by DB and MM5, while CORDEX lacks this parameter. The regional climate model demonstrates a better predictive capability than the DB source, showing slightly higher accuracy in estimating this parameter with better correlations. However, wind direction is generally predicted with the lowest precision due to its changing nature and the difficulties in calculating or recording with good approximation. The observations made for the annual dataset can be extended to the seasonal charts with only a few differences. Temperature and global solar radiation are better described by all sources, although DB and CORDEX exhibit less accuracy during extreme weather conditions, such as those expected in winter (Figure 7c) and summer (Figure 7e). Conversely, MM5 improves its capability to calculate temperature fields on a seasonal scale. Solar radiation performance remains consistent across seasons, with the DB database expected to perform best during summertime. Moreover, [PERSON] and [PERSON]'s method proves to be a valid option for estimating seasonal global solar radiation values. Similarly, MM5 predicts atmospheric pressure better than CORDEX, particularly in spring (Figure 7d), when the latter model performs poorly. The findings regarding relative humidity and wind speed are confirmed on a seasonal scale, with less accuracy expected in predicting wind speed during spring.