## 7 Conclusions  
This study assessed the impact of different climate datasets on dynamic energy simulations and evaluated the adaptation of an educational building in Central Italy to climate change. By comparing historical, current, and future climatic scenarios, the building's heating demands were estimated both in its current state and after a proposed energy-efficient retrofit. In the first phase, both measured and model-generated meteorological datasets were collected and compared to assess their accuracy in predicting key weather parameters like solar radiation, temperature, atmospheric pressure, humidity, wind speed, and wind direction. Different methods for calculating global solar radiation were also tested. The next phase focused on evaluating the energy performance of the reference building and determining its vulnerability to the effects of climate change. The main conclusions of the investigation are summarised as follows:  
* The MM5 dataset proved most reliable for predicting key weather parameters. The DB dataset also showed good performance, especially in estimating solar radiation on both annual and seasonal scales. Moreover, the Campbell and Norman approach was effective for calculating solar parameters, although it slightly underestimated variability. * Energy performance analysis revealed that the older 1976 building section required significantly more energy than newer sections, with monthly consumption differences ranging from 29% to 54% higher. The MM5 dataset providing the most accurate consumption estimates, closely aligning with measured data except for notable deviations in April. The DB and CORDEX databases often overestimated energy consumption, mispredicting monthly values by up to 88% and 199%, respectively. * Energy requirements for the newer NZEB building were significantly lower and showed less sensitivity to different climatic datasets. Again, the MM5 dataset proved to be the most accurate, while DB data generally overestimated energy consumption. * Retrofitting with EPS and glass wool insulation, along with window replacements, showed notable energy reductions in future climate scenarios, with EPS slightly outperforming glass wool. Under future climate conditions, the energy consumption of the 2016 building section is expected to remain stable or slightly decrease due to higher mean temperatures, indicating the building's resilience to climate change. While this study offers valuable insights into the impact of climatic datasets on energy simulations and demonstrates that targeted building retrofits, particularly high-performance insulation and window upgrades, can significantly enhance energy efficiency and resilience to future climate conditions, certain limitations must be acknowledged. First, the study's analysis was based on a restricted selection of climatic datasets and time periods, which may affect the generalisability of the results to other geographical regions. Additionally, the focus was primarily on heating demand, with no consideration of cooling requirements, which are likely to become increasingly important as temperatures rise in future climate scenarios. Additional research should address these limitations and investigate hybrid approaches that combine multiple datasets or incorporate machine learning techniques capable of further improving the accuracy of energy performance simulations. Moreover, assessing the impact of other retrofit strategies, such as integrating renewable energy or advanced materials, would offer a more comprehensive approach to enhancing energy efficiency across diverse climates. Conceptualisation, C.L., S.M., F.S. and V.D. ; methodology, C.L. and S.M. ; investigation C.L. ; writing--original draft preparation, C.L. and S.M. ; writing--review and editing, C.L. ; supervision, S.M., F.S. and V.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Informed Consent Statement:** Not applicable.