Question Answering for Decisionmaking in Green Building Design: A Multimodal Data Reasoning Method Driven by Large Language Models
This work addresses the challenge of specialized knowledge barriers for designers and engineers in green building design, though it is incremental as it builds on existing AI methods.
The study tackled the problem of low decision-making efficiency in green building design by integrating large language models with a multimodal question answering framework called GreenQA, which resulted in 96% of users reporting improved design efficiency in a survey.
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.