As energy consumption continues to rise in modern buildings, improving efficiency has become a critical part of the fight against climate change. Europe is no exception; actions have been taken within the European Union that affect all member states, with initiatives such as the European Green Deal and Horizon Europe placing strong emphasis on sustainable innovation and digitalization. One major focus is the housing sector, which, according to some experts, accounts for 30–40% of all greenhouse gas emissions today. 

The work in this study contributes to the ongoing discussion on how Artificial Intelligence (AI) could be integrated into smart infrastructure to improve energy efficiency and empower users with accessible, competent recommendations. If we are successful in showing this, it could be a huge steppingstone for future work where governments are trying to undergo large adjustments as in the EU where 70% of the existing buildings are built before the year 2000 and renovations are underway within the entire region. 

This study explores whether generative AI can be a viable tool in addressing this issue. Specifically, it examines the extent to which AI is currently used in buildings, given how accessible today’s large language models (LLMs) are to the public, and whether the energy-saving advice they offer is practical and consistent across models. This will then be cross-examined with data gathered from the Swedish National Board of Housing, Building and Planning (Boverket) and real-world building environments is used to analyze energy recommendations patterns from energy experts, predict consumption trends, and evaluate the effectiveness of AI-generated suggestions. 

To explore this, we have evaluated how generative AI models, both untrained and trained with real-world data, perform in proposing standardized energy-saving measures for different building types. The results show that AI can complement existing measures and align with the EU’s broader sustainability goals. The study also highlights differences between the AI services tested, emphasizing the importance of selecting the appropriate model for the intended application.  

Overall, the findings support the conclusion that AI can serve as a practical tool for generating building-specific energy-saving recommendations.