Customized large-scale model for human-AI collaborative operation and maintenance management of building energy systems

Siliang Chen, Xinbin Liang, Ying Liu, Xilin Li, Xinqiao Jin and Zhimin Du

Applied Energy, 2025, vol. 393, issue C, No S0306261925008992

Abstract: Artificial intelligence (AI) is becoming an integral part for operation and maintenance (O&M) management, propelling the low-carbon transition of building energy systems. However, as the users of building energy systems, human beings are detached from the decision-making loop of AI, which leads to the suboptimal performance for O&M tasks in practical applications. To this end, we proposed a customized large-scale model for human-AI collaborative O&M management in building energy systems. The human-AI collaboration mechanism is characterized by humans providing the domain knowledge and specialized tools to guide AI while AI performing tasks to serve human needs. Specifically, the few-shot learning has been utilized in prompt engineering for the customized large-scale model to route various O&M tasks to corresponding solution paths: direct response, retrieving knowledge or invoking tools, which increases the accuracy of task routing from 73.7 % to 90.3 %. For specialized O&M tasks requiring domain knowledge, the multiple semantic levels of energy-specific knowledge are extracted by recursive clustering and integrated into the customized large-scale model by retrieval-augmented generation. The expert questionnaire indicates that the customized large-scale model outperforms ChatGPT-4o in 84 % of survey questions and generates more accurate and concise responses than GLM-4-9B. For complex O&M tasks requiring modeling and computation, the customized large-scale model is integrated with the specialized tools through data-structuring transformation aided by self-supervised reconstruction. The experimental test indicates that the accuracy of the customized large-scale model for fault diagnosis is 96.3 %, which outperforms general large-scale models by over 50 %. Our study will contribute to the human-AI collaboration for more efficient and safety O&M management, thereby accelerating the pace towards net zero emissions in building energy systems.

Keywords: Large-scale model; Human-AI collaboration; Operation and maintenance; Building energy systems; Domain knowledge (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.126169

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