用大模型让绿建设计问答更高效，用户效率提升显著。
Question Answering for Decisionmaking in Green Building Design: A Multimodal Data Reasoning Method Driven by Large Language Models
- 结合大模型与多模态数据，构建可问答的绿建设计辅助框架。
- 96%用户认为平台显著提升设计效率，支持天气分析与案例检索。
- 适合建筑设计师、可持续规划者，助力绿色建筑决策优化。
近年来，绿色建筑在应对能源消耗和环境问题方面的重要性日益凸显。研究表明，超过40%的节能潜力可在设计初期实现。因此，基于建模与性能模拟的绿色建筑决策（DGBD）对降低建筑能耗成本至关重要。然而，该领域涵盖大量专业知识，学习成本高，决策效率低。已有研究将人工智能方法应用于此领域。本文在此基础上创新性地融合大语言模型与DGBD，提出GreenQA——一个面向多模态数据推理的问答框架。通过检索增强生成、思维链和函数调用等方法，GreenQA支持天气数据分析与可视化、绿色建筑案例检索及知识查询等多模态问答任务。此外，本研究通过GreenQA网页平台开展用户调研，结果显示96%的用户认为平台有效提升了设计效率。本研究不仅为绿建决策提供有力支持，也为AI辅助设计提供了新思路。
原文摘要 · Abstract (English)
In recent years, the critical role of green buildings in addressing energy consumption and environmental issues has become widely acknowledged. Research indicates that over 40% of potential energy savings can be achieved during the early design stage. Therefore, decision-making in green building design (DGBD), which is based on modeling and performance simulation, is crucial for reducing building energy costs. However, the field of green building encompasses a broad range of specialized knowledge, which involves significant learning costs and results in low decision-making efficiency. Many studies have already applied artificial intelligence (AI) methods to this field. Based on previous research, this study innovatively integrates large language models with DGBD, creating GreenQA, a question answering framework for multimodal data reasoning. Utilizing Retrieval Augmented Generation, Chain of Thought, and Function Call methods, GreenQA enables multimodal question answering, including weather data analysis and visualization, retrieval of green building cases, and knowledge query. Additionally, this study conducted a user survey using the GreenQA web platform. The results showed that 96% of users believed the platform helped improve design efficiency. This study not only effectively supports DGBD but also provides inspiration for AI-assisted design.
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