RT info:eu-repo/semantics/article
T1 Can ChatGPT AI Replace or Contribute to Experts’ Diagnosis for Renovation Measures Identification?
A1 Hidalgo-Betanzos, Juan Maria
A1 Prol Godoy, Irati
A1 Terés Zubiaga, Jon
A1 Briones Llorente, Raúl
A1 Martín Garin, Alexander
K1 Artificial intelligence
K1 Building energy renovation
K1 Decarbonization
K1 ChatGPT
K1 Renovation measures
K1 Construcción
K1 Building
K1 Tecnología
K1 Technology
K1 Inteligencia artificial
K1 Artificial intelligence
AB 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.
PB MDPI
YR 2025
FD 2025-01
LK http://hdl.handle.net/10259/10206
UL http://hdl.handle.net/10259/10206
LA eng
NO This publication is part of the R+D+i project PID2021-126739OB-C22, financed by MCIN/AEI/10.13039/501100011033/ and “ERDF A way of making Europe”.
DS Repositorio Institucional de la Universidad de Burgos
RD 22-feb-2025