Airtightness is a major issue in architectural design and it has a significant impact on the energy performance of buildings. Moreover, the energy behaviour of built heritage is due, to its singular characteristics, still a great unknown. The aim of this study is to establish a better knowledge of the airtightness of historical buildings, based on an in depth field study using blower-door tests. A set of 37 enclosures were analyzed inside eight buildings located in historical areas of a Spanish city with a significant built heritage. They were constructed between 1882 and 1919 and include diverse construction typologies applied for many building uses such as residential, cultural, educational, administrative and emblematic. The results indicate lower values compared to other previous airtightness studies of historical buildings. The average air change rate was found to be n₅₀ = 9.03 h⁻¹ and the airtightness of the enclosures presented a wide range of between 0.68 and 37.12 h⁻¹. Three main levels of airtightness were identified with two thirds of the tested samples belonging to the intermediate level between 3–20 h⁻¹. To conclude, several correlations have been developed which provide a method to estimate air leakage and could serve as a basis for energy performance studies of these kinds of building.

Building energy renovations demand expertise from professionals to guide processes, including diagnostics, project planning, interventions, and maintenance. The emergence of open-access AI, like ChatGPT in November 2022, offers new possibilities for improving these processes by assisting or potentially replacing human experts. This study explores the effectiveness of ChatGPT in diagnosing energy renovation measures. Initial assessments involve basic queries to the AI, followed by the inclusion of additional data and secondary questions to gauge its full diagnostic potential. An existing building case from the literature is given to the AI to define the best energy renovation measures. Expert evaluations and comparisons with research-backed solutions assess the AI’s performance using different degrees of questioning details over 60 repetitions. The results indicate that ChatGPT can provide valuable insights and generate comprehensive lists of feasible measures and preliminary cost calculations and payback, but, in general, it lacks depth and quality without specialized input and preparation. A significant quality improvement was found between the tests with 2023 and 2024 AI versions. Open-access AI proves capable of enhancing renovation diagnostics but remains a complement rather than a replacement for building renovation expert judgment. This research underscores the potential of mainstream AI to democratize access to knowledge, albeit with limitations tied to its dependence on quality inputs and contextual expertise.