Automated building energy modeling for energy retrofits using a large language model-based multi-agent framework
Jie Lu, Zeyu Zheng, Max Langtry, M.K. Jackson, Yang Zhao, Chenxin Feng, Ruqian Zhang, Chaobo Zhang, Jian Zhang, Ruchi Choudhary
Abstract
Building energy modeling is critical for retrofit design, but it is labor-intensive. We present Data2BEM (Data to Building Energy Model), a large language model-based multi-agent framework that parses architectural drawings, specifications, and sensor data to automatically generate and calibrate building energy simulations. Applied to an existing University of Cambridge office building, Data2BEM produced a calibrated model meeting industry accuracy benchmarks and enabled the assessment of heat-electrification retrofits. Relative to professional practice, the system reduced total modeling time by over 90% (48 min versus 8-32 h) with minimal human input. The workflow integrates information extraction, model generation, and data-driven calibration, delivering end-to-end automation while accurately reflecting measured performance. These results indicate that large language model-driven multi-agent methods can accelerate retrofit analysis, lowering expertise and time barriers for practitioners and supporting scalable pathways to building-sector decarbonization.