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DOE OSTI · 3363609

MCP-enabled agentic AI workflow for building energy modelling: framework and use cases

Li, Han [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)]·Zhang, Liang [Univ. of Arizona, Tucson, AZ (United States)]·Zhou, Huiwen [Univ. of Arizona, Tucson, AZ (United States)]·Hong, Tianzhen [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)]
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Abstract

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.

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32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATIONAgentic AIModel Context Protocol (MCP)building energy modeling (BEM)energy and buildingslarge language model (LLM)

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Li, Han [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)], Zhang, Liang [Univ. of Arizona, Tucson, AZ (United States)], Zhou, Huiwen [Univ. of Arizona, Tucson, AZ (United States)], Hong, Tianzhen [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)]. 2026-04-05. MCP-enabled agentic AI workflow for building energy modelling: framework and use cases. https://doi.org/10.1080/19401493.2026.2653969

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