AI-Powered Automation for Building Control Systems

TLDR: Large language models (LLMs), especially Claude-Sonnet-4, can significantly automate the generation of Modelica-based control modules for buildings, reducing development time by 40-60%. While LLMs excel at generating syntactically correct code with engineered prompts, human experts are still essential for validating behavioral correctness and addressing complex logic errors. The research highlights the economic benefits and future potential of AI in streamlining building control system design.

Developing sophisticated control systems for dynamic energy systems, especially in buildings, is a complex and time-consuming task. Engineers often rely on specialized modeling frameworks like Modelica, an equation-based language, to design and test advanced strategies. However, creating individual control modules within Modelica requires significant expertise and can be very labor-intensive.

The Challenge of Manual Modelica Module Development

The Modelica Buildings Library (MBL) provides a rich set of simulation models for HVAC components, and the Control Description Language (CDL) allows for specifying control sequences using block diagrams. While powerful, the manual development of these CDL modules within Modelica is a bottleneck, demanding specialized knowledge and considerable time. This often limits the widespread deployment of advanced control strategies that could significantly reduce energy use and operating costs.

Introducing AI to Automate Module Generation

Researchers at Pacific Northwest National Laboratory have explored the potential of large language models (LLMs) to automate the generation of CDL modules for building control systems. Their study, titled “Automating Modelica Module Generation Using Large Language Models: A Case Study on Building Control Description Language”, investigates how AI can streamline this intricate process. You can read the full research paper here.

A Structured Approach to AI-Assisted Design

The team designed a structured workflow that integrates LLM capabilities with existing engineering practices. This workflow involves several key components:

Standardized Prompt Scaffolds: Carefully designed prompts guide the LLM to generate relevant code.
Library-Aware Grounding: The LLM is specifically informed about the Modelica Buildings Library to ensure it uses appropriate components.
Automated Compilation: Generated code is automatically compiled using OpenModelica to check for syntax and structural errors.
Human-in-the-Loop Evaluation: Human experts review and validate the generated modules, providing crucial feedback.

Testing LLM Performance: Claude-Sonnet-4 Leads the Way

Experiments were conducted on two types of tasks: basic logic operators (like “And,” “Or,” “Not,” and “Switch”) and more complex building control modules (such as chiller enable/disable, bypass valve control, and cooling-tower fan speed). The results showed a clear distinction in performance among different LLMs:

Claude-Sonnet-4: Achieved an impressive 100% success rate for basic logic blocks when provided with carefully engineered prompts. For the more complex control modules, its success rate reached 83%.
GPT-4o and GPT-4o-mini: Consistently failed to generate executable Modelica code in “zero-shot” mode (without specific context or examples).

This highlights the critical role of prompt engineering and domain-specific grounding in achieving reliable outcomes with LLMs for code generation.

Refining the Generation Process: Hard-Rule Search and Human Oversight

The study also compared different strategies for guiding the LLM. Initially, a Retrieval-Augmented Generation (RAG) approach was explored, but it often led to errors due to semantic overlaps in library documentation (e.g., retrieving “Or” when “And” was requested). To overcome this, a “hard-rule search” strategy was adopted, which explicitly indexed the CDL library and only pulled exact matches for module names. This deterministic approach significantly improved reliability.

Furthermore, while AI could reliably detect compilation errors, its ability to assess the behavioral correctness of the generated modules remained limited. Human experts consistently outperformed AI in verifying if the modules functioned as intended, underscoring that human oversight is still indispensable for validating performance beyond syntax checks.

Significant Productivity Gains

Despite the need for human validation, the LLM-assisted workflow delivered substantial productivity improvements. The average development time for a Modelica module was reduced from an estimated 10–20 hours down to 4–6 hours, representing a 40%–60% time saving. This translates into significant cost reductions, making the development of advanced building control strategies more economically viable.

Future Directions for AI in Building Controls

The research points to several areas for future improvement. These include developing pre-simulation correctness checks for LLMs, enabling automated interpretation of simulation results, and working towards closed-loop validation frameworks where LLMs can iteratively refine modules with minimal human intervention. Improving pipeline efficiency, particularly reducing library loading overheads, is also a practical consideration for wider adoption.

Also Read:

Conclusion

This study demonstrates the significant potential of large language models, particularly Claude-Sonnet-4, to automate and accelerate the generation of Modelica-based control modules for building systems. While human expertise remains crucial for final validation and addressing complex behavioral nuances, the integration of AI into the development workflow offers tangible productivity gains and paves the way for more efficient and trustworthy control system design in the future.