Abstract

Building energy modeling is essential for achieving energy optimization and net-zero targets, yet portfolio-scale analysis faces significant constraints due to critical building information remaining trapped within unstructured documentation such as permits, energy audits, and maintenance records that cannot be systematically extracted and analyzed. This research presents an AI-driven framework utilizing local Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to automatically extract and structure building information while ensuring complete data privacy and security.
The methodology employs on-premises LLM deployment to process sensitive documentation without external data transmission. This approach efficiently transforms diverse unstructured building data into standardized inputs for the Honeybee Python modeling platform, automatically generating detailed energy models and producing comprehensive portfolio-wide performance analytics. The resulting portfolio-wide analytics identify key patterns, inefficiencies, and optimization opportunities across building inventories, while the detailed energy models provide enhanced baseline representations that can be further refined by energy modelers or improved through more sophisticated RAG implementations for deeper analysis.
Validation across 80 Quebec office buildings demonstrated 92.5% processing reliability while reducing analysis timeframes from days or weeks to just a few hours—representing substantial improvements in portfolio assessment efficiency. The framework successfully addresses persistent data processing limitations that have constrained evidence-based energy management, enabling the systematic, data-driven portfolio strategies necessary for achieving ambitious decarbonization objectives.