BEMEval-Doc2Schema: Benchmarking Large Language Models for Structured Data Extraction in Building Energy Modeling

TL;DR

This paper introduces BEMEval-Doc2Schema, a benchmark framework for evaluating large language models' ability to extract structured data from building documentation, advancing automated building energy modeling research.

Contribution

It presents the first standardized benchmark with a new metric (KVOR) for assessing LLM performance in building energy modeling tasks, enabling systematic comparison of models.

Findings

Gemini 2.5 outperforms GPT-5 in structured data extraction.

Few-shot prompting improves model accuracy.

Simpler schemas yield higher KVOR scores.

Abstract

Recent advances in foundation models, including large language models (LLMs), have created new opportunities to automate building energy modeling (BEM). However, systematic evaluation has remained challenging due to the absence of publicly available, task-specific datasets and standardized performance metrics. We present BEMEval, a benchmark framework designed to assess foundation models' performance across BEM tasks. The first benchmark in this suite, BEMEval-Doc2Schema, focuses on structured data extraction from building documentation, a foundational step toward automated BEM processes. BEMEval-Doc2Schema introduces the Key-Value Overlap Rate (KVOR), a metric that quantifies the alignment between LLM-generated structured outputs and ground-truth schema references. Using this framework, we evaluate two leading models (GPT-5 and Gemini 2.5) under zero-shot and few-shot prompting strategies across three datasets: HERS L100, NREL iUnit, and NIST NZERTF. Results show that Gemini 2.5 consistently outperforms GPT-5, and that few-shot prompts improve accuracy for both models. Performance also varies by schema: the EPC schema yields significantly higher KVOR scores than HPXML, reflecting its simpler and reduced hierarchical depth. By combining curated datasets, reproducible metrics, and cross-model comparisons, BEMEval-Doc2Schema establishes the first community-driven benchmark for evaluating LLMs in performing building energy modeling tasks, laying the groundwork for future research on AI-assisted BEM workflows.