Large Language Model-Based Method for HVAC System Control Code Automatic Generation

The control code is an essential part of achieving energy-saving control in HVAC systems. Although there are existing methods for automatically generating control code, these methods still largely rely on expert knowledge for intervention. HVAC systems are highly personalized, with varying types of equipment, different quantities of equipment, and diverse data transmission protocols, all of which still require manual processing, resulting in low efficiency and a strong dependence on human input. To address this challenge, a method based on large language models is proposed. The basic idea is to leverage the understanding, reasoning, and coding capabilities of large language models, provide sufficient information to the model using the RAG (Retrieval-Augmented Generation) technique, and validate the accuracy of the code through a simulation model. By iterating repeatedly, the accuracy of the code is improved, ultimately enabling the automatic generation of control code. The evaluation used 173 laboratory rooms from an actual project to verify the efficiency and accuracy of this method. The results show that this method is highly efficient, processing a single room in just 5 min, and performs well under project-specific conditions, with a code generation accuracy of 97.1%.