Conference Agenda

This presentation explores how GenAI technologies can automate and enhance professional learning across multiple dimensions, including building energy modeling workflows (e.g., EnergyPlus model development, debugging, and calibration), HVAC system design and operation (e.g., load calculation, system sizing, and fault diagnostics), and broader data management skills (e.g., data cleaning, transformation, and knowledge retrieval). Specific training applications include intelligent tutoring systems for energy modelers, AI-assisted certification exam preparation, hands-on scenario generation for HVAC engineers, and personalized feedback for building energy analysts.

Building energy modeling (BEM) often requires significant manual effort, limiting its widespread application in design and operational decision-making. To address this challenge, this study proposes EPlus-LLMv2, an open-source, large language model (LLM)-based platform that enables users to automatically generate complex building energy models using natural language. The proposed method enables highly customized LLM-based BEM for complex cases while maintaining computational efficiency. An interactive human-AI interface is also implemented to improve usability for practitioners.