Using AI to Make Learning Building Energy Modeling Faster and Easier

What is it about?

This paper presents a novel educational framework that leverages multimodal large language models (LLMs) to enhance learning and application in building energy modeling. By integrating Retrieval-Augmented Generation (RAG) with a curated dataset of 59 YouTube tutorial videos on EnergyPlus and OpenStudio, the system enables users to receive detailed, context-rich responses to their questions—complete with textual explanations, video timestamps, and screenshots. The backend architecture combines T5 for summarization, Instructor Embedder for generating embeddings, and Meta’s LLaMA v2 7b model for response generation. A lightweight Flask-based web interface delivers these responses in an intuitive format, streamlining the learning curve for architects, engineers, and students. Remarkably, the entire system runs on modest hardware, showcasing its accessibility and scalability for educational purposes.

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Why is it important?

This work is important because it tackles a major barrier in the field of building energy modeling: the steep learning curve and overwhelming volume of technical resources required to master tools like EnergyPlus and OpenStudio. These tools are critical for designing energy-efficient, net-zero buildings, yet they remain inaccessible to many students and practitioners due to their complexity. By leveraging multimodal LLMs and Retrieval-Augmented Generation, this research provides a fast, intuitive way to access precise, visual, and context-rich answers—drastically reducing the time and effort needed to learn energy modeling. It democratizes access to high-performance building design knowledge and empowers a broader audience to contribute to sustainable architecture.

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