@article{bibcite_36943,
  author = {Han Li and Liang Zhang and Huiwen Zhou and Tianzhen Hong},
  title = {MCP-enabled agentic AI workflow for building energy modelling: framework and use cases},
  abstract = {<p>Traditional building energy modelling workflows remain labor-intensive and error-prone, requiring specialized expertise that limits broader adoption. This paper introduces a novel Model Context Protocol (MCP)-enabled framework that connects AI assistants to EnergyPlus through MCP, a standardized interface for tool invocation and context management. Two complementary integration paradigms are presented and compared: conversational integration, where users interact through natural language while an AI assistant orchestrates MCP tools on demand, and agentic workflow integration, where specialized agents coordinate autonomously to complete multi-step tasks. Using an experimental testbed for residential buildings, the end-to-end workflows are demonstrated. The conversational approach reduced typical inspection and modification tasks from 1-2 h to under 15 min, while maintaining full transparency through visible tool invocations. The agentic approach automated parametric analysis. These demonstrations establish MCP as a foundational layer for AI-assisted building energy modelling, enabling natural language interactions with simulation tools while preserving professional oversight and decision-making authority.</p>},
  year = {2026},
  booktitle = {Journal of Building Performance Simulation},
  journal = {Journal of Building Performance Simulation},
  series = {Journal of Building Performance Simulation},
  pages = {1-27},
  month = {05/04/2026},
  institution = {Informa UK Limited},
  publisher = {Informa UK Limited},
  issn = {1940-1493, 1940-1507},
  url = {https://doi.org/10.1080/19401493.2026.2653969},
  doi = {10.1080/19401493.2026.2653969},
}