How Large Language Models (LLMs) Are Helping Our Buildings Become More Efficient

As buildings become increasingly complex, so does the challenge of operating them efficiently. From managing energy systems to meeting climate targets, the built environment generates massive amounts of data, but data alone doesn’t create efficiency. This is where Large Language Models (LLMs) are beginning to play a transformative role.

LLMs help turn fragmented, technical building data into insights that humans can actually act on. By analysing inputs from energy models, building management systems (BMS), sensors, utility bills, and maintenance logs, LLMs can quickly identify inefficiencies that would otherwise take weeks of manual analysis. This allows building owners and operators to spot unusual energy spikes, optimise system schedules, and respond to performance issues in near real time.

In the design and retrofit phase, LLMs support better decision-making by accelerating scenario analysis. They can summarise and compare retrofit options, interpret building energy modelling outputs, and translate technical results into clear recommendations that balance cost, carbon, comfort, and constructability. This is especially valuable in retrofit projects, where constraints vary widely, and every building behaves differently.

LLMs are also improving building operations and maintenance. By learning from historical work orders and operational data, they can predict recurring issues, recommend preventive maintenance actions, and reduce downtime. Instead of reacting to equipment failures, facilities teams can move toward proactive, performance-driven management, saving energy, money, and time.

Beyond operations, LLMs reduce administrative and compliance burdens. They can assist with sustainability reporting, energy code documentation, and compliance checks by automating data synthesis and narrative reporting. This makes it easier for teams to align with building standards, ESG requirements, and climate disclosure frameworks without adding overhead.

Ultimately, LLMs act as a bridge between advanced building technologies and the people who manage them. By translating complex information into clear guidance, they enable faster decisions, smarter retrofits, and more efficient buildings. As AI continues to mature, LLMs will increasingly support the shift from static, underperforming buildings to adaptive, low-carbon, high-performance assets.

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References

He, T., & Jazizadeh, F. (2025). Context-aware LLM-based AI agents for human-centered energy management systems in smart buildings. arXiv. https://arxiv.org/abs/2512.25055 (arXiv)

Khadka, S., & Zhang, L. (2024). Scaling data-driven building energy modelling using large language models. arXiv. https://arxiv.org/abs/2407.03469 (arXiv)

Zhang, M., Liu, M., Wang, H., Wen, Y., Luo, A. L., & Zhang, Y. (2025). Leveraging large language model for generalization in building energy management. IEEE Transactions on Smart Grid, 16(6), 4712–4725. https://doi.org/10.1109/TSG.2025.3589202 (Monash University)

Jiang, G., Ma, Z., Zhang, L., & Chen, J. (2024). EPlus-LLM: A large language model-based computing platform for automated building energy modeling. Applied Energy, 367, 123431. https://doi.org/10.1016/j.apenergy.2024.123431 (ScienceDirect)

Zhang, L., Chen, Z., & Ford, V. (2024). Advancing building energy modeling with large language models: Exploration and case studies. Energy and Buildings, 323, 114788. https://doi.org/10.1016/j.enbuild.2024.114788 (ScienceDirect)

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