用大模型自动设计可解释的建筑能耗预测规则
BuildEvo: Designing Building Energy Consumption Forecasting Heuristics via LLM-driven Evolution
- 用大模型驱动演化，融合建筑物理特性与运行数据生成预测规则
- 在多个基准上达到顶尖性能，且泛化能力更强
- 适合需要透明、可靠能耗预测的智能建筑与能源管理场景
精准的建筑能耗预测至关重要，但传统启发式方法精度不足，而先进模型常因缺乏可解释性且忽略物理规律导致泛化能力差。本文提出BuildEvo框架，利用大语言模型（LLM）自动设计高效且可解释的能耗预测启发式规则。通过进化过程，该框架引导LLM系统性地整合建筑特征与运行数据（如来自建筑数据基因组项目2的数据），构建并优化预测规则。评估表明，BuildEvo在多个基准上表现领先，兼具优异的泛化能力与透明的预测逻辑。本工作推动了鲁棒、物理一致的启发式规则自动化设计，为复杂能源系统提供可信建模支持。
原文摘要 · Abstract (English)
Accurate building energy forecasting is essential, yet traditional heuristics often lack precision, while advanced models can be opaque and struggle with generalization by neglecting physical principles. This paper introduces BuildEvo, a novel framework that uses Large Language Models (LLMs) to automatically design effective and interpretable energy prediction heuristics. Within an evolutionary process, BuildEvo guides LLMs to construct and enhance heuristics by systematically incorporating physical insights from building characteristics and operational data (e.g., from the Building Data Genome Project 2). Evaluations show BuildEvo achieves state-of-the-art performance on benchmarks, offering improved generalization and transparent prediction logic. This work advances the automated design of robust, physically grounded heuristics, promoting trustworthy models for complex energy systems.
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