This is like giving an AI a rough description of a building and letting it draft, check, and fix the energy simulation model the way a smart junior engineer would—only much faster and on repeat.
Building energy models (for design, code compliance, and performance analysis) are time‑consuming to set up and debug manually; this workflow uses LLM-based agents to automatically create and troubleshoot those models, reducing expert labor and speeding up analysis cycles.
Domain-specific workflows and prompts for building energy modeling, plus integration with established simulation tools and access to historical model/debug data, could form a defensible advantage.
Hybrid
Context Window Stuffing
High (Custom Models/Infra)
Context window limits and inference cost when handling large, complex building models and long debug traces.
Early Adopters
Unlike conventional building energy modeling tools that rely on manual configuration, this approach wraps a general-purpose LLM in an agentic workflow to automatically generate and iteratively debug models, reducing expert intervention and making high-fidelity simulation more accessible to non-specialists.
104 use cases in this application